Mti swing trader course20

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THESE RESULTS ARE BASED ON SIMULATED OR HYPOTHETICAL PERFORMANCE RESULTS THAT HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE THE RESULTS SHOWN IN AN ACTUAL PERFORMANCE RECORD, THESE RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, BECAUSE THESE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THESE RESULTS MAY HAVE UNDER-OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED OR HYPOTHETICAL TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THESE BEING SHOWN.

****SPREADS/COMMISSIONS NOT TAKEN INTO ACCOUNT IN THE ABOVE RESULTS****

How to develop atrading strategy in excel

How to develop atrading strategy in excelThings You'll Need

Importing Data

Select any cell in an Excel spreadsheet with the cursor. On the Menu Bar select "Data-Import External Data-New Web Query." This will open a dialog box that allows you to direct Excel to a website from which data can be imported. In the address bar of the browser in the dialog box, type "finance. yahoo" and click on the "Go" button.

Type in the ticker symbol for one of the stocks you are watching in the website's search bar. The dialog box has the browser inside it. Search for quotes as though you were using the website. This will be the ticker symbol imported into Excel.

Check the box that highlights the "Last Trade." At the bottom of the dialog box, click "Import." Another dialog box will appear asking where you want to import the data. The cell that was originally highlighted should be visible, i. e. "=$A$1." If this is correct, click OK.

Hide rows that are not desired to be seen on the spreadsheet. The import tool will import eight pieces of data. Any data that is not relevant to the research must be hidden, not deleted. If the data/rows are deleted, Excel will produce errors the next time you attempt to import data. To hide data, highlight the applicable rows, right-click on them and select "Hide".

Stock Analysis Formulas

Calculate the growth rates of categories such as Earnings Per Share (EPS), Sales, Cash Flow and Equity by using Excel's "=Rate" formula. Excel will calculate the growth rate of any two given values over a given period of time.

The formula looks like this: =rate(nper, pmt, pv, [fv], [type], [guess]). The "nper" is the number of years being calculated. The "pmt" is not used in this calculation. The "pv" is the beginning value (inputted as a negative number). The "[fv]" is the ending value. The "[type]" and [guess]" values are also ignored for this calculation.

To determine the EPS growth rate over the past 10 years for a company that has risen from 2.2 to 4.5, you would input the following calculation: =rate(10,,-2.2,4.5), giving an EPS growth rate of 7%.

Calculate the future value of a stock to determine a purchase price by using Excel's future value (FV) formula, "=FV(rate, nper, pmt, [pv], [type])". The "rate" is the growth rate calculated in Section 2, Step 1. The "nper" is the number of years out to predict. The "pmt" is not used. The "[pv]" is the starting value (inputted as a negative number).

To determine what the stock price of a company that has a current stock price of $14 and a growth rate of 7% is going to be in five years, you would use this formula: =FV(7%,10,,-14), giving a stock price of $27.54 in five years at the current growth rate.

Organize your spreadsheet to import stock price data and hide the unneeded data. Use Excel's formulas to determine each company's past growth rates in the columns adjacent to the share price. Then use the above formulas to determine target purchase prices for each stock in the next adjacent column.

Conditional Formatting

Utilize conditional formatting. Imagine if you were tracking the stock prices of 100 companies and had buy or sell prices associated with each stock. It would take a considerable amount of time to manually scan and filter through all the data to determine if there was a buy or sell in a portfolio. Conditional formatting allows the user to quickly identify cells that meet certain criteria. When that criterion is met, Excel will highlight the cell.

Click on a cell and type in the actual share price of a stock. In the next adjacent cell, input the target buy price of the stock. Click back on the cell containing the actual share price. As this number changes, Excel will compare it to the target buy price and highlight it if it drops to it or below. On the Menu Bar click "Format and Conditional Formatting." The Conditional Formatting dialog box will open.

Set the first drop-down menu to "Cell Value Is" and the second drop-down menu to "less than or equal to". On the third drop-down menu, click the "get data" button on the right side of the box, allowing you to choose the data you want to compare. In this case, click on the cell that had the target buy price in it and click OK. The Conditional Formatting dialog box will reappear.

Click the "Format" button to open the "Format Cells" dialog box. Click the "Patterns" tab to choose the color the cell is to be highlighted if the cell meets the criteria placed on it. The font and other formatting can be changed here by clicking the "Font" tab. The conditions are in place and there should be no highlighted cells on the spreadsheet. As the stock price changes in the "actual share price" cell, if its price drops below the "target buy price" cell, Excel will highlight the cell.

Change the value in the "actual stock price" cell to a number below the target buy price. The cell will be highlighted, indicating it has met the conditions that have been set identifying it as a stock to buy.

Set up conditional formatting for all stocks on your spreadsheet that contains the imported data and target price calculations. When a stock price meets your investing strategy's criteria, that cell will be highlighted, alerting you to a possible investment opportunity.

Anti-money laundering test

Anti-money laundering testAnti-Money Laundering Test

For many smaller firms, it is not possible to achieve the requirement of independence where the person conducting the test cannot perform or be supervised by any person who performs any of the tasks to administer the AML program. Larger firms either require or prefer to have a third party to perform the test of this critical function. ComplianceWorks staff provides an independent and expert test of a firms anti-money laundering program. The test consists of reviewing documents and conducting interviews of AML staff and supervisory personnel which address the following areas:

Risk Assessment does the firm conduct a risk assessment of its AML program; and if so are the risks identified addressed in the written procedures?

Written procedures test to determine if the written procedures of the firm are up to current standard. AML procedures seem to change every year and firms are expected to remain abreast of the changes and implement new procedures as required.

Customer Identification Program (CIP) test to determine if the firm has the critical components to identify customers on account opening and to perform maintenance review of customers. Does the CIP provide procedures based on risk of client and account types?

Suspicious Person Review test to ensure the firm has adequate procedures to review suspicious persons lists; test to insure that the firm responds to FinCEN requests.

Suspicious Transactions and Activity Reporting test customer files to determine if CIP is fully and meticulously implemented, determine if the firm has certain customers that present more AML risk. Tests on transactions to look for activity that displays characteristics of money laundering; test to ensure that the firm follows up on any activity identified as suspicious; test to determine if the firm has adequate SAR reporting procedures.

Notice to Customers test to determine if the required disclosures are made to customers regarding the firms AML program.

AML Training test to determine if the firm conducts AML training that is customized to the firms specific operations.

AML Testing has the firm maintained a consistent AML testing program and has the firm corrected any deficiencies highlighted by testing; has senior management signed off on the AML test and the AML program?

Test results are discussed with firm personnel to allow for further input and to discuss noted deficiencies. A final written report is provided that fulfills the required evidence of AML testing required by rule.

How to use arbitrage opportunities in commodities

How to use arbitrage opportunities in commoditiesHow to use arbitrage opportunities in commodities

Financial markets offer a host of trading options for investors with different risk profiles. While one can opt for various market strategies, such as trading, arbitrage and long-term investing, an interesting, low-risk option is arbitrage. It's an opportunity which can help an investor benefit from the difference in the prices of an asset on various platforms. Arbitrage helps reduce the price disparity of an asset in different markets even as it helps boost the liquidity.

There are two pre-requisites for exploiting an arbitrage opportunity. One, that the asset trades at different prices in different markets, exchanges or locations, and two, that two assets with identical cash flows should not trade at the same price. In case of commodities, too, a market participant can avail of various types of arbitrage opportunities.

Some of the major strategies that you can use in arbitrage are:

- Cash-n-carry - Spread - Inter-exchange - Inter-commodity

Here's how you can use these different types of arbitrage strategies for trading in commodities.

Cash-n-carry

Cash-n-carry arbitrage can be used between spot/physical and future prices of a commodity. This strategy is often used by commodity traders who have linkages with physical markets. In this case, arbitrageurs set up a trade in the physical market and, simultaneously, take a position in the futures market in order to gain from the price disparity between the spot and futures prices.

Suppose an arbitrageur finds that in January 2014 the price of wheat in the physical market is around Rs 1,500 per quintal. On the other hand, in the futures market, the price of wheat in February expiry contract is around Rs 1,550 per quintal. So, he can buy the commodity in the physical market and, simultaneously, sell in the futures market.

At the time of expiry, he can settle the future trade by giving the delivery of physical wheat at Rs 1,550 a quintal. In this trade, he can make a profit of Rs 50 a quintal after deducting the applicable charges.

In case of spread, arbitrageurs trade only in the futures contracts on exchanges to benefit from the price differentiation between various contracts of the same commodity. They buy a futures contract and sell another futures contract of the same underlying commodity on the exchange to profit from the price difference.

For example, the gold February 2014 contract is trading at around Rs 29,000 per 10 g and the next contract of gold April 2014 is trading at around Rs 28,500 per 10 g. Now, the arbitrageur can set up an arbitrage trade in two different scenarios. If he thinks that at the time of expiry of the February contract, the current difference of Rs 500 could reduce further, he can sell the gold February futures contract and buy the April futures contract.

At the time of expiry of the gold February contract, he can square up the trade and book a profit of Rs 500 per 10 grams. If he thinks that at the time of expiry of the February contract, the difference of Rs 500 will increase, then he can buy the gold February contract and sell the April contract. At the time of the expiry of gold February contract, he can square up the trade and book a profit of Rs 500 per 10 grams.

This is also a technique to set up an arbitrage trade in the commodity market. The price difference for the same commodity on various exchanges with the same contract expiry can be exploited as an interexchange arbitrage opportunity. The price difference for the same commodity in the two exchanges can arise due to volatility, liquidity and contract specifications, among other reasons.

For instance, if the price of CPO January 2014 futures contract is around Rs 541 per 10 kg on the MCX, and Rs 545 per 10 kg on the NCDEX, an arbitrageur can buy it on the MCX and sell on the NCDEX, thereby making a profit of Rs 5 per 10 kg.

Inter-commodity

When one considers a different commodity on the same exchange having the same cash flow or in the same category, then an inter-commodity arbitrage can be created. For instance, an arbitrage between cotton, cottonseed, cotton oilseed cake and kapas can be created in order to benefit from the price difference.

Take the example where the price of cottonseed in the February 2014 futures contract is around Rs 1,950 metric ton on the NCDEX, and the price of cottonseed oil cake in the February contract is around Rs 1,560 per metric ton.

Currently, the price difference between the commodities is Rs 390 per metric ton. Now, if the arbitrageur thinks that the difference will increase or decrease as per the market condition, he can buy the cottonseed February contract and sell the cottonseed oil cake February contract in case of an expected rise in the difference, and vice versa.

(The writer is Associate Director, Commodities & Currencies, Angel Broking)

Option trading strategies bull call spread

Option trading strategies bull call spreadTrade Example

We are in Januarty, 2012 and after some analysis, an options trader is reasonably bullish on Yahoo stock which is currently trading at $15.48.

He purchases the "YHOO Jul 12, 13 Call" currently trading at $3.35 and simultaneously sells the "YHOO Jul 12, 18 Call" currently trading at $1. This implies that, assuming the trader purchases one contract of each, he would have to pay $336 for the long call and receive $100 for the short call. Therefore the net cost incurred in entering the position is $236.

Now fast forward to the July, 2012, when the option expires. Assuming Yahoo is now trading at $19. This means that both calls expire in-the-money and will be exercised. The intrinsic value for the "YHOO Jul 12, 13 Call" will be $6 per share (100 in total), therefore, we receive $600. On the other hand we payout $100 for the "YHOO Jul 12, 18 Call". The total pay-off for this bull call spread trade is therefore, 600 - 100 - 236 = $264.

Conversely, if the price of Yahoo had actually fallen to $12 at expiry, both calls would have expired out-of-the-money which means they would not be exercised. The total loss would therefore be limited to the net cost of entering the position, in the case of this example, -$236.

You may also find the following interesting:

Fibonacci trading video course by the fibmaster!

Fibonacci trading video course by the fibmaster!Fibonacci Trading Video Course By The FibMaster!

Learn this powerful Fibonacci Retracement method FREE that pulls 500+ pips per trade plus download the Fibonacci Trading Video Course just now that can make you can expert at trading with fibonacci.

Get this highly profitable Magic Breakout Forex Strategy by Tim Trush and Julie Lavrin FREE. Watch this shocking 24 minutes FREE Presentation on the Forex Profit Multiplier Method that takes only 60 seconds to find high probability trades on the 6 major pairs and can be repeated multiple times a day whenever you got a few minutes. It made 384+ pips in 12 hrs.

Fibonacci trading confuses many traders. No matter what market you trade whether it is forex, stocks, futures, options, commodities or ETFs, Fibonacci levels work very well. If you are serious in making money as a trader than you must master the art of Fibonacci trading.

There is a lot of mumbo jumbo about the Fibonacci Trading spread by the trading gurus in the market. Most of them have intentionally made this stuff too complicated. When a new trader tries to learn these concepts, he or she simply gets confused. This course will remove the confusion from your mind if you have been searching for a good material on Fibonacci Trading.

Why do these levels work? Simple, everyone from the pro traders to the hedge funds and institutional investors are using these fib levels to enter and exit the market. But there is more to it than this simple explanation. When you will watch these 21 video lessons on Fibonacci Trading by Neal Hughes, you will understand why these fib levels work so well to predict the turning points in the market.

Neal Hughes is a pro trader who has been trading for 20 years and using Fibonacci Analysis as his main trading tool. In these videos, he will start from the very basics and first introduce you to this very important trading tool. Then he will take you step by step to the next higher level. In the end, he will show you the advanced Fibonacci Trading Techniques that the pro traders use. Now in these videos, you can get the answer to most of the common questions that traders ask when they try to use Fib Levels like:

1. How to calculate the turning points in the market?

2. How to calculate retracements?

3. How to calculate the extensions and projections?

4. Where should you enter and exit the market?

5. Where to place the stop loss?

6. How to determine strong support and resistance?

7. How to find the high probability trade setups?

8. How to stack the odds in your favor?

Plus much more. Neal Hughes gives you 60 days no question asked money back guarantee. You can try his Fibonacci Trading Course RISK FREE for 60 days and if these Fibonacci Trading Videos are not the easiest to learn or if it does not improve your trading and help you start making winning trades, you can simply go for a refund.

Trading binary option strategies and tactics bloomberg financial free download excel-best binary o

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Turtle trading rules

Turtle trading rulesTurtle Trading Rules

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Invented by a market guru called Richard Dennis. Turtle Trading is a mechanical system that has, in the past, been enormously profitable, and is thought by the general public to be an almost infallible trading system. As a style, it requires a particular mindset, as you will suffer a great number of small losses, and only a few large wins. This can be so disheartening that traders following this strategy either give up, or cherry pick, missing the irregular huge winners that offset the numerous small losers. Very deep pockets and inordinate patience are required for this strategy to work for you. It is definitely no use for day trading timescales.

Hope you remember the story: The turtle Wins the Race.

The Books available in the markets about turtle tradings are,

Denver stock broker jobs

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Sar forex indicator

Sar forex indicatorSAR Forex indicator

SAR Forex indicator finds the long-term uptrend or downtrend and allows to take a big profit, similarly Forex Secret Protocol. Sometimes profit one position can reach 700 pips! SAR Forex indicator is universal and can be used on all pairs and timeframes.

Сharacteristics of SAR Forex indicator

Platform: Metatrader4

Currency pairs: Any

Trading Time: Around the clock

Timeframe: Any, recommended H1 or higher

Recommended broker: Alpari

Buy entry, when the indicator SAR Forex painted green horizontal line. Exit - appears red vertical line:

Sell entry, when the indicator SAR Forex painted yellow horizontal line. Exit - appears red vertical line:

Online trading academy kansas city

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Forex indicators an important tool in optimizing trading strategy

Forex indicators an important tool in optimizing trading strategyForex Indicators: an important tool in optimizing trading strategy

Forex indicators are data points that indicate the direction in which a currency will move. Forex indicators are used extensively by investors to optimize their trading strategies. These indicators are used across timeframes and currency pairs. The right mix of a variety of indicators may help one formulate an effective trading strategy that succeeds in a dynamic and fast-moving currency market.

Forex Indicators: types

Broadly speaking, Forex indicators can be classified into two categories:

Leading technical indicators: These suggest the probability of what is likely to happen in the Forex market with respect to the direction in which a particular currency pair is headed or where a currency pair price would reach.

Lagging technical indicators: These indicators keep traders abreast of what has already happened in the Forex market. These indicators are useful in identifying whether the market is moving sideways or is trending up or down.

Forex Indicator Tool: the ones to look out for

Some of the key Forex indicators are:

Simple Moving Averages (SMA): This indicator tells a trader the average price for a particular time period, for example five minutes, 20 minutes, one day, etc. Each of the chosen periods has the same weight.

Exponential Moving Average (EMA): The averages, under this indictor, are calculated with the recent Forex rates carrying a higher weight in the entire average. This is done in order to obtain a more accurate indication of trend direction.

Relative Strength Index (RSI): It is a price-following oscillator that has a range of 0-100. One of the more frequently used methods of analyzing the RSI is to find a divergence at which the currency price touches a new high, but the RSI is not able to surpass the previous high. This diversion points towards an impending reversal.

Moving Average Convergence/Divergence (MACD): This involves the plotting of two momentum lines. The MACD line is the difference between two exponential moving averages and the trigger/signal line, which is basically an exponential moving average of the difference.

Stochastic Oscillator: This indicates overbought or oversold conditions on a range of 0%-100%. The indicator is based on the occurrence that, in an uptrend, the closing prices for particular periods are concentrated in the top segment of the period’s range. Conversely, in a downtrend, the closing prices are concentrated in the lower segment of the period’s range.

Although this is not an exhaustive read of all the possible Forex Indicator Tool that a trader needs to keep track of, these are some of the main ones that would help formulate better trading strategies.

Strategy map

Strategy mapStrategy Map

A strategy map is a visual summary of what a company plans to do in order to improve its business, gain more customers and improve its financial outlook.

Additionally, many companies find strategy maps valuable because they include intangible factors like culture in the strategic analysis. A strategy map provides multiple perspectives when evaluating a company and helps in the strategy's effective execution.

Benefits of Strategy Maps

Strategy maps let your company's leadership team clearly visualize their goals and communicate them with the rest of the company.

Set clear financial and customer goals.

Visualize the connections between various ideas and show how those ideas could lead to specific results.

Identify the necessary parts of the organization that will support new undertakings and changes including any necessary training and business process changes

Communicate the company's goals and show how they will be achieved.

Provide a starting point for every each division and see how they fit into the overall strategy.

How to Create a Strategy Map

A strategy map looks similar in structure to a swim lane diagram but the concepts it deals with are more similar to those found on a balanced scorecard.

Strategy maps are divided into horizontal lanes that represent different perspectives to evaluate. These perspectives are typically Financial, Customer, Internal Business Process, and Learning and Growth, following the typical balanced scorecard divisions. For each category, brainstorm ways to improve that aspect of your business and place your ideas inside boxes and ovals. Connect the ideas with arrows to illustrate relationships between the goals and changes listed.

Forex jobs uk

Forex jobs ukForex trading jobs in uk

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What are the benefits of online trading

What are the benefits of online tradingMore from the nerds

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What Are the Benefits of Online Trading?

Published on September 14, 2014

Online trading is easy and quick. You can educate yourself on your investment options, place orders to buy and sell, and possibly make (or lose) a considerable amount of money without ever speaking with a broker or leaving the comfort of your home. As with any investment strategy, there are benefits and risks involved. So, why are more people playing the market through online trading?

It (almost) eliminates the middleman.

Years ago, you couldn’t make a trade without meeting or at least calling your broker. Now, it takes only a few clicks. This accessibility could certainly make online trading alluring for those who may not have had the finances or the connections to work with a full-service broker in the past. Online traders can buy and sell without ever speaking to a broker. This doesn’t mean trading is done with no broker input, as discount brokerages actually facilitate the trade when you click the mouse. However, online trading allows you to trade with virtually no direct broker communication.

It’s cheaper.

Having a broker execute your trades for you costs money. And while you’ll pay for online trades. the cost won’t be as high. As more brokerages allow online access, the prices continue to drop, with many of the popular discount brokerages offering trades for under $10 each and some requiring no account minimum.

It offers greater investor control.

Online traders can trade when they want. In conventional trading, an investor may have to work with a delay depending on when she is able to contact her broker and when the broker is able to place her order. Online trading allows nearly instantaneous transactions. Also, investors are able to review all of their options instead of depending on a broker to tell them the best bets for their money.

You can monitor your investments in real time.

Online brokerages offer advanced interfaces and the ability for investors to see how their money is performing throughout the day. Log in through your phone or your computer and you can see any gains or losses in real time. These brokerages also offer more tools for traders of all levels, posting not only finance news but also providing analytic platforms and research reports.

Potential disadvantages

The main drawback of online trading is for many people the very thing that makes it so alluring: no broker input. Unless you are a well-informed investor, analyzing the markets and determining when and what to trade is a risky endeavor. There is also the risk that the technology could work against you; simple interfaces mean trades and errors are both easy to make.

The disadvantages of online trading are fading as brokerages see the need to make processes as user-friendly as possible. Their websites are offering more tools to educate online traders and even allowing beginners to practice with virtual money before taking the plunge with the real deal. For both experienced traders and those with a limited knowledge of investing, online trading presents one affordable solution.

Forex analysis and trading effective top-down strategies combining fundamental,position,and techni

Forex analysis and trading effective top-down strategies combining fundamental,position,and techniDescription

About the Author

T. J. Marta is Founder and Chief Market Strategist of Marta on the Markets, LLC. He is Editor and Publisher of the daily Morning Minute and a regular contributor to the Overnight Express and FXstreet. T. J. is a respected strategist and speaker with more than 20 years of Wall Street and business experience. Marta uses his technical knowledge, market experience and passion for history to provide not only context for global developments, but more importantly, the investment implications of those developments.

T. J. has served as a US trading floor economist, fixed income strategist and G10 currency strategist at two of the largest financial services firms in the world: RBC and Citigroup. His professional affiliations include the National Association for Business Economics and the Money Marketeers of NYU's Stern School. Marta holds a BS from the University of Pennsylvania's Wharton School and an MBA from the Stern School at New York University.

Joseph Brusuelas is Director at Moody's Economy and is a well-known economist in the financial sector. Prior to his current position, he was the Chief Economist at Merk Funds and Chief U. S. Economist at IDEAglobal. His primary fields of interest are monetary policy, fixed income, currencies and commodities. Brusuelas was named the best forecaster during the month of August 2009 by Marketwatch/Dow Jones.

Product Description

The forex market is huge and offers tremendous trading opportunities. There are many different tools for analyzing the forex market. But what are the best tools and the best ways to use them to trade most effectively?

Forex Analysis and Trading organizes the most widely used—although disparate—approaches to forex analysis into one synergistic, robust, and powerful framework. This system draws on fundamental, position, and technical analyses to identify profitable currency positions, enabling traders to make the best decisions regarding major currencies.

Marta and Brusuelas are forex trading professionals with years of experience analyzing and trading every major currency.

Strategic human resource development-powerpoint ppt presentation

Strategic human resource development-powerpoint ppt presentationStrategic Human Resource Development - PowerPoint PPT Presentation

PPT Strategic Human Resource Development PowerPoint presentation | free to view - id: 928e7-NDcwY

Strategic Human Resource Development

Emerging. Needs/ Feedback. Measures. Of. Effectiveness. Global OD Services. Staff of 15 (instructional designers, trainers, OD consultants). PowerPoint PPT presentation

Title: Strategic Human Resource Development

Strategic Human Resource Development

K. Peter Kuchinke

Professor, Human Resource Development

University of Illinois at Urbana-Champaign

Yuan-Ze University, April 2008

Outline

Strategic HRD

Context, models, and definition

Applications

Human Capital Formation, Organizational

Learning, and Implications for Life-long

Learning Policies

Case Examples

The New HRD

Employees as org. assets

Driving business strategy

Spanning organizational functions

HRD Deliverables

Performance

Capacity Building

Problem solving/consulting

Org. change and development

The New HRD

Employees as org. assets

Driving business strategy

Spanning organizational functions

HRD Deliverables

Performance

Capacity Building

Problem solving/consulting

Org. change and development

Strategic HRD

Integration of HRD with strategy formulation and

implementation

Long-term view of HR policy

Horizontal integration among HR functions

Vertical integration with corporate strategy

SHR as core competitive advantage

Firm Capitals

Human Capital

Knowledge, skills, abilities of individuals

Social Capital

Relationships in social networks

Structural, cognitive, relational dimensions

Intellectual capital

Knowledge and knowing capability of social

collectivities

Procedural/declarative tacit/explicit

individual/social

Value and Uniqueness of capitals

Multiple Roles for HR (Ulrich, 1997)

Future/Strategic Focus

Mgmt of Trans - Formation/Change

Mgmt of SHR

Mgmt of Firm Infrastructure

Day-to-day/Operational Focus

Definition of HR Roles

Universal/Best Practice Models

TQM

Corporate culture, communications,

voice/involvement, job design, training,

performance measurement/evaluation, rewards,

health/safety, selection/promotion, career

development

Peters and Waterman In search of excellence

Org. culture, leadership, customer focus, core

competency

High involvement management (Lawler)

Developing skills and knowledge, pay for

performance, investment in HR, flexible

operations, self-designing work systems,

autonomous work-teams

Universal HR Models

Pfeffer (1998)

Employment security

Selective hiring

Self-managed teams/decentralization of

decision-making

Comparatively high pay linked to firm performance

Extensive training

Reduction of status differentials

Shared information

Quality Awards (M. Baldrige, State Awards, etc.)

HR Focus (work systems, education/training,

well-being and satisfaction)

Human Capital Architecture

Uniqueness of HC High

Quadrant 1 Emplt Mode Internal

development Emplt Rel. Organization focused HR

Configuration Commitment

Quadrant 4 Emplt Mode Alliance Emplt Rel.

Backtesting trade systems

Backtesting trade systemsRe: Backtesting trade systems

I have been working on such a package too. I am currently testing it, and

was planning to have a small user group release in about a month or so.

>From the vignette introduction (that I am still expanding):

The goal of the package > is to provide such a

framework using an object oriented approachfootnote

ef for a discussion on the design decision.>. There

are two main classes. An class from which all user

algorithms inherit and a class used for performace

tracking and reporting. By convention, the parent classes

and are capitalized, while lower case names, say

algo1> and are used for instances of these classes.

Conceptually, a model is represented by an algorithm and a trader.

The functionality provided in this package allows the user to define

multiple trading algorithms. He could gradually refine these

algorithms by using the inheritance mechanism, or use the same

algorithms with different parameters to instantiate different

code objects.

The code object contains all the historical data necessary, a

set of parameters, functions that generate trading signals, and a set

of functions that modify the positions given a trading signal. Using

an algorithm , the user can create a that uses

the algoritm over a specified time interval to arrive at a trading

decision for each time step. A trade log is kept by the trader, so

> I had a look at Rmetrics, blotter, fTrading, PerformanceAnalytics, backtest,

> quantmod, TTR etc, but not one of these fill my requirements. It's not that

> So I have decided to build my own solution, reusing as much as possible from

> these existing packages. (As a former software engineer I know how much time

Yield curve trading strategies pdf

Yield curve trading strategies pdfYield curve trading strategies pdf

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Newsletterpro-forex opportunities

Newsletterpro-forex opportunitiesNewsletterPro Forex Opportunities

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Retail forex trading is anegative sum game

Retail forex trading is anegative sum gameRetail Forex Trading is a Negative Sum Game

Other opinions on the same debate:

A little while ago, Richard Olsen authored an article titled Trading Forex Is A Positive Sum Game. In it he talked about how both sides to a trade derive some kind of value – referred to as utility - from doing the transaction. Using the broad definition of utility, Richard is certainly correct. We each get something out of doing a trade, though we may not always know exactly what that is at the time. It’s like Ed Seykota’s famous comment from the original Market Wizards book – “Win or lose, everybody gets what they want out of the market.”

I do have to disagree with the article in one respect, though. Dr. Olsen is a well-regarded researcher in academic circles, but he was incorrect in one aspect of his discussion. He made the case that even if we look at forex trading strictly in financial terms (gains and losses) it can still be positive sum. His argument is two-fold. First, he makes the case that market participants can come at the game from different perspectives (like long - vs. short-term) and that the markets are infinite rather than finite multiplayer games. Second, he argues that currency values can rise and fall broadly, increasing or decreasing their holder’s purchasing power as a result.

While these arguments, especially the latter, can be viewed as making a good case for the overall forex market not being zero-sum, they fail in the case of the retail spot forex market, which is likely where most readers of this article operate. I’ll address both here, starting with the second.

There is no currency in the market

The argument that currency holdings can increase or decrease in terms of purchasing power because of the way not only exchange rates, but other prices (gold, oil, etc.), move only works in a market where there actually are currency holdings. This is certainly applicable to the inter-bank spot market where traders actually exchange one currency for another.

Such, however, is not true of the retail forex market because no currency ever gets exchanged there. It is a market without assets. Each transaction is an agreement between parties to do a future exchange - which is the same as in the inter-bank market - but any position held through day-end gets rolled forward, so the agreed upon exchange is perpetually pushed forward until offset by an opposing transaction. Retail spot forex is thus like the futures market (exchange of contracts) more so than the stock market (exchange of assets).

And by the way, the money put into a retail trading account cannot be considered to be “in the market” because it is never used to purchase anything. It is there simply to ensure the trader is good for any losses suffered trading so their broker’s credit risk is limited. It’s not like the down payment on a mortgage or the amount posted for margined stock purchases, as those are actually used to purchase something.

Your gain is my loss

That brings us back to the first part of Dr. Olsen’s case against forex being zero-sum. It ties directly in with the contract-based market structure of retail forex noted above. When we put on a position we enter into an agreement with a counterparty (or more than one) whereby one of us is long and the other short. The one who is long will benefit as the exchange rate rises, while the short will benefit as the exchange rate falls.

same $10,000. If, instead of rising, EUR/USD falls to 1.10, then the situation would be reversed, with you losing $10,000 and me making that same amount.

Notice in this example how the combined net position of you and I doesn’t change. Your gain is my loss, and vice versa. Total wealth between the two of us does not rise or fall. It merely changes hands. It is like a hand of poker where the winner gains all the money in the pot, but since that is just money he and the other players bet, with no additional money being added, the overall wealth at the table doesn’t rise or fall. It merely moves from the losers to the winner. That’s a financial zero - sum game in both cases (though it could be positive sum in terms of broad utility as noted above).

We can’t all win

Now for the subject of players in multiple investment horizons and the idea that everyone can profit. Dr. Olsen makes this case in his article, but I think he was speaking in broader terms, not specifically of retail forex. I’ve seen this argument made many times in the retail context, though, so it needs addressing.

The bottom line is it’s mathematically impossible in the context of retail spot forex trading for everyone to profit. In theory everybody but one trader could be profitable, but not all of them. Because each position has to have both long and short sides, it is a net zero market balance (+x for long + - x for short = 0). It doesn’t matter how many players the game features, the turnover rate, or how long it runs. The market balance must always be zero because of the requirement for there to be a short for every long, and viceversa.

Let me demonstrate.

We’ll start with a simple 2-person transaction whereby Trader A goes long 100,000 EUR/USD at 1.20 with Trader B on the other side. If, as in the example above, EUR/USD rises to 1.30, then Trader A is up $10,000 and Trader B is down $10,000 – no net change in wealth for the overall market.

Let’s say Trader A wants to exit the trade and walk away with his profits. To do that he needs to either get Trader B to end their contract, or to find someone else to do an offsetting trade. He finds the latter in Trader C, so now Trader A is flat with a $10,000 gain, Trader B is short with a $10,000 loss, and Trader C is long with no gain or loss yet. Everything remains balanced.

If EUR/USD continues to rally and rises to 1.40, Trader C now has a $10,000 gain and Trader B has lost another $10,000, so he is down $20,000 overall. Add in Trader A’s $10,000 gain and we’re still at no net gain or loss for the market as a whole. Still zero-sum.

You can have as many traders coming and going as you like, for as long as you like, doing as many transactions as you like. Because every position must entail offsetting long and short sides, it will always balance out. Wealth is never created, just passed around among market participants. It is financially zero-sum.

Actually, in practice retail forex is a negative sum proposition for traders. This is because they are predominantly price takers, so on the wrong side of the bid-ask spread. Some pay commissions to their brokers as well, adding to the cost. Also, there is a bid-ask spread in the carry interest paid/ received when holding positions overnight (traders receive the lower rate for the long currency, pay the higher for the short currency). That means carry interest is also a negative sum factor because Trader A will receive less carry interest than Trader B pays, or vice versa. The result is that wealth is slowly shifted out of the cumulative accounts of the traders and into the hands of the brokers and market makers.

Why does any of this matter?

The fact that retail forex trading is a zero/negative-sum game means it’s a competitive market. This is very different from an asset market like stocks where if you hold a portfolio sufficiently large, or something like an index ETF, you have been virtually guaranteed to have some gain in the long run, even if you have absolutely no skill. Such is not the case in retail forex trading, which is more like poker in that money will tend to transfer from the unskilled players to the skilled players over time.

You may be thinking at this point, “Yeah, but new players keep coming into the market to provide a ready supply of unskilled players to take money from.” That is certainly true. I would, however, make two points.

First, in order to benefit consistently from that stream of new players you actually need to make sure you are both better than them and that you are playing in an arena where you are mostly trading against them. You don’t want to be trading in an arena where it’s mainly those better than you. And because retail forex is not a place where currency hedging goes on, you cannot rely on getting much opportunity to trade against players who don’t really care if they gain or lose.

Second, as has been documented in many places of late, we’re not seeing the same kind of growth in activity in the forex market as we saw for many years. We may be seeing a contraction, in fact. At the same time, we’re seeing a surge in the popularity of so-called social trading (also known as copy trading), which means the proportion of skilled traders (assuming those being copied can be classified as such) in the market is effectively multiplied because of the accounts replicating them. That is a combination which likely will make the market more competitive.

So what should you take away from all this? Well, for one you may decide to look for a non - competitive market to trade (like stocks), or to let someone else trade for you via a social trading setup. If you’re reading this, though, you’ve likely already committed yourself to trading forex. That being the case, make sure to learn from those who have gone before you (both the winners and the losers), and until you have incontrovertible proof that you are among the skilled traders to whom the trading profits tend to flow in the long-run, keep your exposure to the market small. No need to give those stronger traders any more of your money than you have to as you try to join their ranks.

I am aus citizenif iplace my currency trading account into

I am aus citizenif iplace my currency trading account intoI am a US citizen. If I place my currency trading account into

Satisfied Customers: 10679

Experience: Over 8 years of legal practice.

replied 3 years ago.

Thank you for your question. Be sure to go ahead and bookmark nateanswers for future questions.

The IRS will certainly crack down on that behavior and penalize if you do not pay the proper taxes. Technically, if you're an American citizen anywhere in the world, any income you make is considered taxable.

I've seen the trusts you're referencing where someone else owes a fiduciary duty to you and controls it and therefore there is no legal trail leading back to you.

There is no truly 100% legal way to do it. The only thing you can do is do it and hope the IRS does not find out about it. If they do find out about it, they will penalize you and possibly push criminal charges.

Free forex download

Free forex downloadFree Forex Download

Auto Fibonacci Trading Indicator

The indicator will automatically determinate a current trend and print all the information on your chart. Buy Sell Arrows + Alerts + Levels on your charts, Current trading opportunity, entry level, stop loss level, 3 take profit levels etc.

When a trend will change, the Auto Fibonacci Phenomenon indicator will automatically re-print all the entries, SL, TP for a new trend.

Example as shown on the screenshot, current trend is UP. If a trend changes to DOWN trend, the indicator will automatically re-print new level for a down trend (SELL opportunity)

Take profit level 1 (normal)

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Currency Strength Meter MT4 Indicator

Currency Strength Meter for MT4 is indicator that will show you the strength of each currency pair compared to other currency pairs.

The main idea of this indicator is: Buy the strongest currency and Sell the weakest currency. This indicator helps a lot to validate the main trend.

Combine this indicator with Daily Pivot Points or Support/Resistance Indicator, It will give you best profitable entries.

Upload this indicator to > Your MT4 Folder/MQL4/Indicator

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Currency Strength Meter MT4 Indicator

TrueTL Auto Trendline Indicator

How to Install Your Old ex4 Custom Indicator/Expert Advisor on New MT4 Update

I know maybe some of you get a little frustrated when your favourite Custom Indicator/Expert Advisor doesnt work or show up anymore in MT4 Navigator Panel, especially on the latest MT4 update (Version 4.00 Build 600). On the latest version of MT4, It only display Indicators which have the mql4 version, some ex4 indicators still displayed and work, but some others not.

Expert Forex Systems

There are two complete systems revealed in one book . You will learn precisely how the Power Break ™ and the Extreme Swing™ methods work in over 100 pages of detailed, clearly written pages packed with examples from real life trading situations.

There is nothing held back. The complete, entire methods are revealed from A-Z, giving you exactly the same advantage in the market as the few traders who already hold the secrets to these methods.

Remember, these methods have never been revealed anywhere else before in this format, and you will receive instant full knowledge of some of two of the most profitable systems we have ever used.

Set and Forget - Free time galore! Both the methods, The Power Break™ method and the Extreme Swing™ method, take literally only a few minutes a day to analyse, set up and manage. Each system has an exact time of analysis.

Trading strategies pivot points

Trading strategies pivot pointsPivot Points and Variations

The original pivot point was a relatively simple concept the average of the high, low, and close price for a given period was a crucial level for the following period. Working off of this figure, additional areas of support and resistance could be identified.

A move below the pivot point was a bearish signal. while a move upon the pivot point was a bullish signal. Traders would then watch the support and resistance levels for additional insights.

SPY Pivot Points - Source: StockCharts

The original pivot point support and resistance is calculated as follows:

Pivot Point = (High + Low + Close) / 3

Support (S1) = (Pivot Point x 2) High

Support (S2) = P (High Low)

Resistance (R1) = (Pivot Point x 2) Low

Resistance (R2) = P + (High Low)

Over time, technical analysts have taken these basic concepts to develop new variations of the pivot point that incorporate other forms of analysis. Fibonacci Pivot Points, for instance, incorporate the golden ratio to attempt to make more accurate predictions. For example, the Support (S1) in that case is equal to P (0.382 x (High Low)) and Support (S2) is equal to P (0.618 x (High Low)), where 0.382 and 0.618 are variations of the golden ratio.

Demark Pivot Points introduce additional complexity by making the calculations contingent upon the opening price relative to the closing price for the period. In addition to these changes, the Demark style is unique in that it only has one support and resistance level rather than two or more like many other styles. Trader should experiment with these various styles in order to find an option that works best for them and/or a particular security being analyzed.

Profiting from Pivot Points

There are many different ways to use pivot points to enhance trading performance, since they simply identify areas of support or resistance. At their core, pivot points set the tone for price action in a given security by establishing when it’s trading higher or lower than a key level set by the previous period. A stock trading above its pivot point could be considered bullish, while a stock trading below its pivot point could be considered bearish.

The support and resistance levels identified by pivot points can also be interpreted as key levels for traders to watch. For example, a trader may buy a stock that breaks out from its pivot point on high volume and put in a take-profit order when it reaches R1 resistance. Traders may also set stop-loss points for a position just below support levels in order to avoid losses if a stock begins to move significantly lower following an adverse event.

A final consideration when using pivot points is timeframes. Since pivot points can be calculated across any timeframe, it’s important for traders to look at more than just a single timeframe during their analysis. A 15-minute chart may show one key pivot point level, but a 1-day chart may show something entirely different. In addition, different equities may respond to different pivot points in different ways, which makes it important to look deeper.

The Bottom Line

Pivot points are a very useful form of technical analysis that were initially used by floor traders to set important price levels to watch during the day. Over time, the same concept has split into a variety of different techniques involving the same premise. Traders use these pivot points to identify potential areas of support and resistance and, combined with other forms of technical analysis, these insights can help improve trading performance over time.

If you’ve enjoyed this article, sign up for the free TraderHQ newsletter ; we’ll send you similar content weekly.

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CONTENIDO DEL CURSO

• Historia del Mercado de Forex

• Forex, un Mercado flotante libre

• Mayores participantes en el Mercado de Forex

2. Teoria Basica del Mercado de Forex

• Pares

• Pares mas recomendados

• Porque el transar el Mercado de Forex

• Como funciona el volumen del Mercado de forex

• Como analizar el Mercado de Forex

• Como se hacen las ganancias en el Mercado de Forex

• Forex un Mercado de doble via

3. Evaluacion Y prediccion del Mercado de Forex

• Analisis Fundamental (economia 101)

• Analisis tecnico

• Analisis Fundamental vs analisis Tecnico

4. Opciones

• Introduccion sobre opciones y futuros

• Caracteristicas de la opciones

• Opciones exoticas vs opciones registradas bajo el CFTC

• Deficit de cuenta corriente

• Oferta y demanda

• Tasa de interes

• Resistencias y soportes

• Promedios moviles

• Tendensias de alza tendencias de baja

• Graficas de candlestick y de barras

• Patrones de Mercado

8. Indicadores tecnicos

9. principos de un administrador de riesgo

• Tener suficiente dinero de acuerdo a los contratos que adquiere

Forex trading brokers in india

Forex trading brokers in indiaNew To Forex Trading In India? Approach Professional Brokers Today!

No matter whether you have googled them out or someone in your friend circle, peers or family are already practicing them, money making methods always remain the inspiration of ambitious people like you. Of the many such ways that may embellish your bank accounts with additional currencies, forex trading in India is the latest one.

The method is a bit different from other online money churning businesses since it is neither based on any survey nor does it require you to do any affiliate marketing. Instead, it is based on your understanding and knowledge in the forex domain. The more you know about foreign exchange, the regulation of foreign currencies, changing values of currencies in the international market, etc. the better you are expected to rake in respectable moolah.

However, do not fret if you are a beginner or get the goose bumps by the mere thought of investing money online in this most sought after trading vertical of the modern times. Seeking assistance of forex brokers in India could well help you dish out of the testing situations arising in your brave attempts. Their expertise and efficiency in ensuring huge profits from the forex market may come handy in enabling you accomplish your wish of earning smart returns significantly.

Special attention is hence required to assure that the company or individual you have chosen in the best among the forex brokers in India . A better ploy would be to check out for their registration, their reputation in the market, past experience, benefits they have ensured to other investors, whether beginners or experienced ones and so on.

Once you are aware of the market trends with the help of expert forex trading professionals, you may go ahead with your own intelligence and start investing in the market yourself. All you need is to have your own forex account and you are all set to go. Best wishes!

Why Should Indian Investor Take Forex Market Seriously?

One common advice that fund managers give is that investors should always diversify their portfolio. This is especially true for investors in India. Have you ever heard the term Forex Fund Managers in India . If you are like 99% of Indian Investors, the chances are very slim. So who is this individual? How can he/she help investors in India diversify their portfolio?

The secret to this lies in the sector they operate in. Across the world, trading in currencies has been a lucrative market for investors. However, the penchants for gold and shares have kept Indian investors away from trading in forex or foreign currency. Another main reason is the concept of dealing in currency not native to India.

However, what they do not realize is that forex trading can be extremely rewarding. Let us examine the reasons for the same.

Amount required to invest is much lower: We all know that the levels required for investing in gold and shares are on the higher side. In contrast, the levels for forex trading are much lower. This allows investors to use forex as a method of secondary investment.

Wide variety of trading options: Unlike the stock market, you have a larger number of trading options. You can swap currencies, cover the difference between the selling and buying price, decide before-hand when you want to cash in, etc.

Trade online any time both night and day: You can conduct your forex trading from your laptop at home or office either in the day or at night. This ensures that you do not miss out because of your regular work.

As you can infer, trading in forex is much more convenient and simplified. However, it is essential that you use authorized forex trading brokers in India for every transaction.

Understanding the Basics of Forex Trading

As an Indian investor, I stayed away from Forex trading. I did not understand the concept and hence did not realize that I was ignoring an excellent investment option. It was only when my friend opened a forex mini account in forex company India at my name, did I start to explore this new concept of trading. So what did I find? Was it good, better or bad? Rather than get into those, let me explain the concept of forex trading for other investors like me.

What is the meaning of Forex?

Forex basically means trading in currency. As Indians, we are used to trading heavily in shares/stocks, gold, etc. However, currency trading has also been around for a long time but not that popular. In fact, this form of investment could assure better monetary outcomes than shares and gold. The decision by traders to bet on which currency however depends on its current state in the global economy and the local economy.

What is the exchange rate used?

Since we are dealing with currencies, a factor called interbank rate is worthwhile discussing. This rate is different from the selling and buying rate fixed by the Reserve Bank of India. It is an average rate under which the currency can be traded and is fixed in a manner so as to protect the currency from wild fluctuations.

Is there any terminology that I should know?

When it comes to forex trading, there are some terminologies that you should be aware of:

Spread . It is the difference between the buying and selling value of the currency.

Spot . Refers to a direct cash based exchange of currencies.

Futures . Refers to a contract where currencies can be traded at a future date between two traders.

Options . Here traders can exchange one currency for another at a pre-determined date and rate.

All these important guidelines for online forex trading for beginners could help you get good returns in the long run hence there is no harm in following them.

Seek Expert Advice on Forex Investments

As a concept for investment, forex is still relatively unknown in India. The fact is that very few investors seek out Forex Fund Managers in India . This is basically due to the settle mindset of the investors in this part of the world who prefer relying on mutual funds and pension schemes with an alarming degree of exclusivity. Let’s have a look at the forex market in India and try to decipher how people can take advantage of this new and form of Investment for their betterment.

To help understand this concept better, we need to look at both the positive and negative aspects.

Positive aspects:

Low investment requirement: Unlike the share market, investors can begin trading with much lower amounts. This helps them control the risk factor.

Anytime trading: Forex trading can be done round the clock, which is in contrast with trading that is done only in fixed timings.

Trading is entirely online: Most investors still prefer the offline method of trading. However, forex trading has given new options in the hands of the investors as it can be done online in entirety.

Higher degree of volatility: Forex trading is much more volatile than any other investment. This may lead to extreme results – either huge gains or huge losses.

Low Internet speeds: India is not known for high Internet speeds, which could lead to delay in information or incomplete transactions.

High possibility of fake traders: Since this is a new concept; the number of unscrupulous elements in this market is on the higher side. You can lose your entire investment in one click.

Just like any other investment mode, seeking out for a genuine forex company India is always a suitable option in the wake of safeguarding your investment.

Online Foreign Currency Trading-How to start?

In order to be a successful trader, one needs to keep in mind several things. Nowadays, because of technology the scope of trading as widened. Initially people only dealt in trading of stocks and shares of domestic companies. Later with the advent of the internet, the international market also opened with a bang. And for even more adventurous traders, online foreign exchange trading has provided them with a huge gateway.

It is perceived that online trading can help you make big money is less time. This may be true to an extent; however it comes with its risk. In order to make big profits one has to be very careful and conduct proper market research before entering a trade. A better ploy would be to find experienced currency trading brokers in India . especially for beginners.

There are many options, as to how can a traders hedge risk. Its just that one needs to study in-depth about the market conditions. If you are a newbie then it is advisable to start with mini account trading where in you can control your lot size and end up hedging risk. Another way to seek assistance in time of needs is to take the help from currency trading broker in India.

These brokers act as an intermediary between the actual terminal and the trader. They not only help in opening and operating the account, but also provide you with updates and strategies which come very handy. Another important consideration is to test the waters with a small lot and then enter with a big lot so that you are in safe hands.

An alarming situation arises when people out of haste and excitement tend to involve in online foreign exchange trading and end up making huge losses. To avoid such adverse situation, one must work and coordinate in a group of currency trading brokers in India as that they can hedge risk and help you realize profits.

Ways to Find Forex Brokers in India

While you start trading, you are likely to face loads of apprehensions as a beginner. The same accounts for forex currency trading system as well. Hence, before moving ahead with your trading ventures in this field, it is advisable to seek help from a forex broker India who acts as an intermediary between the live terminal and the trader. Not only does he help you start with the trading, but also acts like a helping hand at every step.

But finding a befitting broker is not that easy task amid the crowded market where everyone claims to be the best. So wondering how to find an efficient forex broker who charges minimum cut and provides you with maximum reward? The below mentioned few tips are just apt to satisfy your concern:

Check whether the broker you are looking to associate with is financially regulated or not. This would ascertain that you are working with the right entity and your funds are in safe hands.

Market conditions are very uncertain, if today it is low, tomorrow it might suddenly rise as well. This is known as volatility and it is this volatility, which makes some enjoy profits and others suffer losses. Keeping this fact in mind, brokers generally offer two kinds of spreads namely fixed spreads and floating spreads. Those who offer fixed spread guarantee a pre-decided spread irrespective of the market condition. However, brokers who offer a floating spread determine a rate according to the market conditions. It is up to the investor to weigh the pros and cons and select whichever they feel is better.

Do a bit of research on the online foreign currency trading system they are using before finalizing on the broker. Try and opt for one who uses easy to understand trading platforms than who use complicated platforms.

Ways to Perform Better in Online Foreign Exchange Trading

Foreign exchange trading has come a long way. There was a time when internet was available to only a handful, however today; almost anyone can reap the benefits of the internet and technology. Not surprisingly, this has highly influenced the forex currency trading system in India as well.

Notably, trading refers to buying and selling of stocks, shares, bond etc. It depends on the ability of a trader as to what he wants to invest in. There are two kinds of traders namely risk takers and risk averters and online foreign exchange trading system acts as a suitable platform for both of them. Not only this platform helps the in trading but also enhances the knowledge of interested traders that comes handy in increasing their profit margin.

Many have a perception that trading is a vicious circle; however, this is not true. If a trader consciously makes an effort he would always be able to release more gains than losses. Gains and losses are a part of this game and a smart trader will always learn strategies and ways to make hay when the sun is shining. In fact, one must learn the art of leveraging market conditions whenever suitable.

There are many currency trading brokers in India who work actively in shortening the gap between the trader and the market. It is advisable to get in touch with one such experienced trader to help you perform a wide number of activities. Some important activities thus include opening a trading account, trading on your behalf, providing you with various tips and strategies to maximize your gains, and so on.

Practice makes a man perfect and the same holds for trading as well. There are many trading platforms for forex currency trading which provide you with demo accounts. These are designed to provide practice and act as a replica of the live terminal. As a newbie, you can practice and attain confidence through these replica platforms before moving to a live online foreign exchange trading terminal.

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Fast moving average crossovers

Fast moving average crossoversCombining SMAs and EMAs »

Fast Moving Average Crossovers

You will learn about the following concepts

If you have any questions or suggestions you are welcome to join our forum discussion about Fast Moving Average Crossovers .

We have spoken much about moving averages throughout our trading guides, particularly in the articles Introduction to Moving Averages , Simple Moving Average and Exponentially Smoothed Moving Average , so now we will turn our attention toward moving average crossover strategies. You can have crossovers between fast moving averages, between slow moving averages or a mix between the two types. Here we will discuss fast moving average crossovers.

For this strategy we will use a 15-minute time frame and three exponential moving averages a 10-, a 25- and a 50-period one. The entry and exit rules are simple. Once the 10-period EMA penetrates the 25-period EMA, continues and crosses through the 50-period one, enter in the 10-period EMAs direction. However, before executing the trade you need to wait for confirmation, which comes after a bar closes on the other side of the 50-day EMA.

You hold on to your position until one of the following two scenarios occur: the 10-period EMA crosses back either the 25-period EMA, or its retracement extends further to the 50-period EMA. The latter exit strategy carries the chance of better results because the price might reverse and head back in the desired direction before crossing the 50-day EMA. However, if it doesnt, your profit would be smaller in comparison to a 25-period EMA exit. In both cases, an exit out of the position should be done after a confirmation, i. e. after a bar closes beyond the 25 or 50-period EMA.

Trend required

Moving average crossovers are a reliable trading strategy, but only in trending markets. They have none-to-zero value in conditions of sideways trading. Due to the EMAs small periods, their crossovers achieve good results during strong breakouts and overall strong moves. Their main disadvantage is that they produce many false signals during trading ranges and, second, because moving averages are a lagging indicator, they have little predictive value. Thus, the trader needs to constantly keep an eye on his opened position and act as market conditions shift. Check out the following example.

As you can see on the 15-minute chart example above, the 10-period EMA (yellow) crossed the other two (blue 25-period, and red 50-period) on several occasions. First, at (1) . the EUR/USD cross marked a major decline on the base of an economic data release, causing a fast moving average crossover. Traders who would wait for a confirmation (a bar closing below the 50-period EMA) would enter at the close of the big bear trend bar, while some may have dropped to a lower time frame (10 or 5 minutes) and search for a close there. They would then have exited either at (2) where the 10-period EMA crossed the 25-period essentially at breakeven, or at bar (3) which would have been a loss. This stands to show how fairly ineffective this trading system is during conditions of sideways trading (the market was in a trading range prior the breakout at (1) and almost immediately after the climactic bear trend bar it entered a barbed wire trading range ).

Our example, however, provided us with an EMA crossover in trending conditions as well. At (3) . the 10-period EMA crossed above both slower EMAs shortly after a big bull trend bar broke out of the tight trading range. The following bull trend would become evident after the formation of three very strong bull trend bars. Here is how the trend unfolded afterwards.

As you can see, our entry was around the 1.3550-1.3552 area. The 10-period EMA did not cross below the 25-period EMA up until (1) . where some traders would have closed their positions. Alternatively, some who hoped the upward move will be extended would have waited for a 50 EMA crossover, which happened at (2) and the first bar below it closed at 1.3605. Even with the riskier exit approach, you would have earned little over 50 pips.

If you have any questions or suggestions you are welcome to join our forum discussion about Fast Moving Average Crossovers .

Quantstrat trader

Quantstrat traderReview: Invoances TRAIDE application

This review will be about Inovance Techs TRAIDE system. It is an application geared towards letting retail investors apply proprietary machine learning algorithms to assist them in creating systematic trading strategies. Currently, my one-line review is that while I hope the company founders mean well, the application is still in an early stage, and so, should be checked out by potential users/venture capitalists as something with proof of potential, rather than a finished product ready for mass market. While this acts as a review, its also my thoughts as to how Inovance Tech can improve its product.

A bit of background: I have spoken several times to some of the companys founders, who sound like individuals at about my age level (so, fellow millennials). Ultimately, the selling point is this:

Systematic trading is cool.

Machine learning is cool.

Therefore, applying machine learning to systematic trading is awesome! (And a surefire way to make profits, as Renaissance Technologies has shown.)

While this may sound a bit snarky, its also, in some ways, true. Machine learning has become the talk of the town, from IBMs Watson (RenTec itself hired a bunch of speech recognition experts from IBM a couple of decades back), to Stanfords self-driving car (invented by Sebastian Thrun, who now heads Udacity), to the Netflix prize, to god knows what Andrew Ng is doing with deep learning at Baidu. Considering how well machine learning has done at much more complex tasks than create a half-decent systematic trading algorithm, it shouldnt be too much to ask this powerful field at the intersection of computer science and statistics to help the retail investor glued to watching charts generate a lot more return on his or her investments than through discretionary chart-watching and noise trading. To my understanding from conversations with Inovance Techs founders, this is explicitly their mission.

However, I am not sure that Inovances TRAIDE application actually accomplishes this mission in its current state.

Heres how it works:

Users select one asset at a time, and select a date range (data going back to Dec. 31, 2009). Assets are currently limited to highly liquid currency pairs, and can take the following settings: 1 hour, 2 hour, 4 hour, 6 hour, or daily bar time frames.

Users then select from a variety of indicators, ranging from technical (moving averages, oscillators, volume calculations, etc. Mostly an assortment of 20th century indicators, though the occasional adaptive moving average has managed to sneak innamely KAMAsee my DSTrading package, and MAMAaka the Mesa Adaptive Moving Average, from John Ehlers) to more esoteric ones such as some sentiment indicators. Heres where things start to head south for me, however. Namely, that while its easy to add as many indicators as a user would like, there is basically no documentation on any of them, with no links to reference, etc. so users will have to bear the onus of actually understanding what each and every one of the indicators they select actually does, and whether or not those indicators are useful. The TRAIDE application makes zero effort (thus far) to actually get users acquainted with the purpose of these indicators, what their theoretical objective is (measure conviction in a trend, detect a trend, oscillator type indicator, etc.)

Furthermore, regarding indicator selections, users also specify one parameter setting for each indicator per strategy. E. G. if I had an EMA crossover, Id have to create a new strategy for a 20/100 crossover, a 21/100 crossover, rather than specifying something like this:

short EMA: 20-60

long EMA: 80-200

Quantstrat itself has this functionality, and while I dont recall covering parameter robustness checks/optimization (in other words, testing multiple parameter setswhether one uses them for optimization or robustness is up to the user, not the functionality) in quantstrat on this blog specifically, this information very much exists in what I deem the official quantstrat manual, found here. In my opinion, the option of covering a range of values is mandatory so as to demonstrate that any given parameter setting is not a random fluke. Outside of quantstrat, I have demonstrated this methodology in my Hypothesis Driven Development posts, and in coming up for parameter selection for volatility trading.

Where TRAIDE may do something interesting, however, is that after the user specifies his indicators and parameters, its proprietary machine learning algorithms (WARNING: COMPLETELY BLACK BOX) determine for what range of values of the indicators in question generated the best results within the backtest, and assign them bullishness and bearishness scores. In other words, looking backwards, these were the indicator values that did best over the course of the sample. While there is definite value to exploring the relationships between indicators and future returns, I think that TRAIDE needs to do more in this area, such as reporting P-values, conviction, and so on.

For instance, if you combine enough indicators, your rule is a market order thats simply the intersection of all of the ranges of your indicators. For instance, TRAIDE may tell a user that the strongest bullish signal when the difference of the moving averages is between 1 and 2, the ADX is between 20 and 25, the ATR is between 0.5 and 1, and so on. Each setting the user selects further narrows down the number of trades the simulation makes. In my opinion, there are more ways to explore the interplay of indicators than simply one giant AND statement, such as an OR statement, of some sort. (E. G. select all values, put on a trade when 3 out of 5 indicators fall into the selected bullish range in order to place more trades). While it may be wise to filter down trades to very rare instances if trading a massive amount of instruments, such that of several thousand possible instruments, only several are trading at any given time, with TRAIDE, a user selects only *one* asset class (currently, one currency pair) at a time, so Im hoping to see TRAIDE create more functionality in terms of what constitutes a trading rule.

After the user selects both a long and a short rule (by simply filtering on indicator ranges that TRAIDEs machine learning algorithms have said are good), TRAIDE turns that into a backtest with a long equity curve, short equity curve, total equity curve, and trade statistics for aggregate, long, and short trades. For instance, in quantstrat, one only receives aggregate trade statistics. Whether long or short, all that matters to quantstrat is whether or not the trade made or lost money. For sophisticated users, its trivial enough to turn one set of rules on or off, but TRAIDE does more to hold the users hand in that regard.

Lastly, TRAIDE then generates MetaTrader4 code for a user to download.

And thats the process.

In my opinion, while what Inovance Tech has set out to do with TRAIDE is interesting, I wouldnt recommend it in its current state. For sophisticated individuals that know how to go through a proper research process, TRAIDE is too stringent in terms of parameter settings (one at a time), pre-coded indicators (its target audience probably cant program too well), and asset classes (again, one at a time). However, for retail investors, my issue with TRAIDE is this:

There is a whole assortment of undocumented indicators, which then move to black-box machine learning algorithms. The result is that the user has very little understanding of what the underlying algorithms actually do, and why the logic he or she is presented with is the output. While TRAIDE makes it trivially easy to generate any one given trading system, as multiple individuals have stated in slightly different ways before, writing a strategy is the easy part. Doing the work to understand if that strategy actually has an edge is much harder. Namely, checking its robustness, its predictive power, its sensitivity to various regimes, and so on. Given TRAIDEs rather short data history (2010 onwards), and coupled with the opaqueness that the user operates under, my analogy would be this:

Its like giving an inexperienced driver the keys to a sports car in a thick fog on a winding road. Nobody disputes that a sports car is awesome. However, the true burden of the work lies in making sure that the user doesnt wind up smashing into a tree.

Overall, I like the TRAIDE applications mission, and I think it may have potential as something for the retail investors that dont intend to learn the ins-and-outs of coding a trading system in R (despite me demonstrating many times over how to put such systems together). I just think that there needs to be more work put into making sure that the results a user sees are indicative of an edge, rather than open the possibility of highly-flexible machine learning algorithms chasing ghosts in one of the noisiest and most dynamic data sets one can possibly find.

My recommendations are these:

1) Multiple asset classes

2) Allow parameter ranges, and cap the number of trials at any given point (E. G. 4 indicators with ten settings each = 10,000 possible trading systems = blow up the servers). To narrow down the number of trial runs, use techniques from experimental design to arrive at decent combinations. (I wish I remembered my response surface methodology techniques from my masters degree about now!)

3) Allow modifications of order sizing (E. G. volatility targeting, stop losses), such as I wrote about in my hypothesis-driven development posts.

4) Provide *some* sort of documentation for the indicators, even if its as simple as a link to investopedia (preferably a lot more).

5) Far more output is necessary, especially for users who dont program. Namely, to distinguish whether or not there is a legitimate edge, or if there are too few observations to reject the null hypothesis of random noise.

6) Far longer data histories. 2010 onwards just seems too short of a time-frame to be sure of a strategys efficacy, at least on daily data (may not be true for hourly).

7) Factor in transaction costs. Trading on an hourly time frame will mean far less PL per trade than on a daily resolution. If MT4 charges a fixed ticket price, users need to know how this factors into their strategy.

8) Lastly, dogfooding. When I spoke last time with Inovance Techs founders, they claimed they were using their own algorithms to create a forex strategy, which was doing well in live trading. By the time more of these suggestions are implemented, itd be interesting to see if they have a track record as a fund, in addition to as a software provider.

If all of these things are accounted for and automated, the product will hopefully accomplish its mission of bringing systematic trading and machine learning to more people. I think TRAIDE has potential, and Im hoping that its staff will realize that potential.

Thanks for reading.

NOTE: I am currently contracting in downtown Chicago, and am always interested in networking with professionals in the systematic trading and systematic asset management/allocation spaces. Find my LinkedIn here.

A Filter Selection Method Inspired From Statistics

This post will demonstrate a method to create an ensemble filter based on a trade-off between smoothness and responsiveness, two properties looked for in a filter. An ideal filter would both be responsive to price action so as to not hold incorrect positions, while also be smooth, so as to not incur false signals and unnecessary transaction costs.

So, ever since my volatility trading strategy, using three very naive filters (all SMAs) completely missed a 27% month in XIV. Ive decided to try and improve ways to create better indicators in trend following. Now, under the realization that there can potentially be tons of complex filters in existence, I decided instead to focus on a way to create ensemble filters, by using an analogy from statistics/machine learning.

In static data analysis, for a regression or classification task, there is a trade-off between bias and variance. In a nutshell, variance is bad because of the possibility of overfitting on a few irregular observations, and bias is bad because of the possibility of underfitting legitimate data. Similarly, with filtering time series, there are similar concerns, except bias is called lag, and variance can be thought of as a whipsawing indicator. Essentially, an ideal indicator would move quickly with the data, while at the same time, not possess a myriad of small bumps-and-reverses along the way, which may send false signals to a trading strategy.

So, heres how my simple algorithm works:

The inputs to the function are the following:

A) The time series of the data youre trying to filter

B) A collection of candidate filters

C) A period over which to measure smoothness and responsiveness, defined as the square root of the n-day EMA (2/(n+1) convention) of the following:

a) Responsiveness: the squared quantity of price/filter 1

b) Smoothness: the squared quantity of filter(t)/filter(t-1) 1 (aka Rs return. calculate) function

D) A conviction factor, to which power the errors will be raised. This should probably be between .5 and 3

E) A vector that defines the emphasis on smoothness (vs. emphasis on responsiveness), which should range from 0 to 1.

Heres the code:

This gets SPY data, and creates two utility functionsxtsApply, which is simply a column-based apply that replaces the original index that using a column-wise apply discards, and sumIsNa, which I use later for counting the numbers of NAs in a given row. It also creates my candidate filters, which, to keep things simple, are just SMAs 2-250.

Heres the actual code of the function, with comments in the code itself to better explain the process from a technical level (for those still unfamiliar with R, look for the hashtags):

The vast majority of the computational time takes place in the two xtsApply calls. On 249 different simple moving averages, the process takes about 30 seconds.

Heres the output, using a conviction factor of 2:

And here is an example, looking at SPY from 2007 through 2011.

In this case, I chose to go from blue to green, orange, brown, maroon, purple, and finally red for smoothness emphasis of 0, 5%, 25%, 50%, 75%, 95%, and 1, respectively.

Notice that the blue line is very wiggly, while the red line sometimes barely moves, such as during the 2011 drop-off.

One thing that I noticed in the course of putting this process together is something that eluded me earliernamely, that naive trend-following strategies which are either fully long or fully short based on a crossover signal can lose money quickly in sideways markets.

However, theoretically, by finely varying the jumps between 0% to 100% emphasis on smoothness, whether in steps of 1% or finer, one can have a sort of continuous conviction, by simply adding up the signs of differences between various ensemble filters. In an uptrend, the difference as one moves from the most responsive to most smooth filter should constantly be positive, and vice versa.

In the interest of brevity, this post doesnt even have a trading strategy attached to it. However, an implied trading strategy can be to be long or short the SPY depending on the sum of signs of the differences in filters as you move from responsiveness to smoothness. Of course, as the candidate filters are all SMAs, it probably wouldnt be particularly spectacular. However, for those out there who use more complex filters, this may be a way to create ensembles out of various candidate filters, and create even better filters. Furthermore, I hope that given enough candidate filters and an objective way of selecting them, it would be possible to reduce the chances of creating an overfit trading system. However, anything with parameters can potentially be overfit, so that may be wishful thinking.

All in all, this is still a new idea for me. For instance, the filter to compute the error terms can probably be improved. The inspiration for an EMA 20 essentially came from how Basel computes volatility (if I recall, correctly, it uses the square root of an 18 day EMA of squared returns), and the very fact that I use an EMA can itself be improved upon (why an EMA instead of some other, more complex filter). In fact, Im always open to how I can improve this concept (and others) from readers.

Thanks for reading.

NOTE: I am currently contracting in Chicago in an analytics capacity. If anyone would like to meet up, let me know. You can email me at ilya. kipnisgmail, or contact me through my LinkedIn here.

How well can you scale your strategy?

This post will deal with a quick, finger in the air way of seeing how well a strategy scalesnamely, how sensitive it is to latency between signal and execution, using a simple volatility trading strategy as an example. The signal will be the VIX/VXV ratio trading VXX and XIV, an idea I got from Volatility Made Simples amazing blog. particularly this post. The three signals compared will be the magical thinking signal (observe the close, buy the close, named from the ruleOrderProc setting in quantstrat), buy on next-day open, and buy on next-day close.

Lets get started.

So heres the run-through. In addition to the magical thinking strategy (observe the close, buy that same close), I tested three other variantsa variant which transacts the next open, a variant which transacts the next close, and the average of those two. Effectively, I feel these three could give a sense of a strategys performance under more realistic conditionsthat is, how well does the strategy perform if transacted throughout the day, assuming youre managing a sum of money too large to just plow into the market in the closing minutes (and if you hope to get rich off of trading, you will have a larger sum of money than the amount you can apply magical thinking to). Ideally, Id use VWAP pricing, but as thats not available for free anywhere I know of, that means that readers cant replicate it even if I had such data.

In any case, here are the results.

Equity curves:

Log scale (for Mr. Tony Cooper and others):

My reaction? The execute on next days close performance being vastly lower than the other configurations (and that deterioration occurring in the most recent years) essentially means that the fills will have to come pretty quickly at the beginning of the day. While the strategy seems somewhat scalable through the lens of this finger-in-the-air technique, in my opinion, if the first full day of possible execution after signal reception will tank a strategy from a 1.44 Calmar to a .92, thats a massive drop-off, after holding everything else constant. In my opinion, I think this is quite a valid question to ask anyone who simply sells signals, as opposed to manages assets. Namely, how sensitive are the signals to execution on the next day? After all, unless those signals come at 3:55 PM, one is most likely going to be getting filled the next day.

Now, while this strategy is a bit of a tomato can in terms of how good volatility trading strategies can get (they can get a *lot* better in my opinion), I think it made for a simple little demonstration of this technique. Again, a huge thank you to Mr. Helmuth Vollmeier for so kindly keeping up his dropbox all this time for the volatility data!

Thanks for reading.

NOTE: I am currently contracting in a data science capacity in Chicago. You can email me at ilya. kipnisgmail, or find me on my LinkedIn here. Im always open to beers after work if youre in the Chicago area.

NOTE 2: Today, on October 21, 2015, if youre in Chicago, theres a Chicago R Users Group conference at Jaks Tap at 6:00 PM. Free pizza, networking, and R, hosted by Paul Teetor, whos a finance guy. Hope to see you there.

Volatility Stat-Arb Shenanigans

This post deals with an impossible-to-implement statistical arbitrage strategy using VXX and XIV. The strategy is simple: if the average daily return of VXX and XIV was positive, short both of them at the close. This strategy makes two assumptions of varying dubiousness: that one can observe the close and act on the close, and that one can short VXX and XIV.

So, recently, I decided to play around with everyones two favorite instruments on this blogVXX and XIV, with the idea that hey, these two instruments are diametrically opposed, so shouldnt there be a stat-arb trade here?

So, in order to do a lick-finger-in-the-air visualization, I implemented Mike Harriss momersion indicator .

In other words, this spread is certainly mean-reverting at just about all times.

And here is the code for the results from 2011 onward, from when the XIV and VXX actually started trading.

Here are the equity curves:

Long-short:

Long-only:

Short-only:

With the following statistics:

NOTE: I am currently contracting and am looking to network in the Chicago area. You can find my LinkedIn here.

A Review of DIY Financial Advisor, by Gray, Vogel, and Foulke

This post will review the DIY Financial Advisor book, which I thought was a very solid read, and especially pertinent to those who are more beginners at investing (especially systematic investing). While it isnt exactly perfect, its about as excellent a primer on investing as one will find out there that is accessible to the lay-person, in my opinion.

Okay, so, official announcement: I am starting a new section of posts called Reviews, which I received from being asked to review this book. Essentially, I believe that anyone thats trying to create a good product that will help my readers deserves a spotlight, and I myself would like to know what cool and innovative financial services/products are coming about. For those whod like exposure on this site, if youre offering an affordable and innovative product or service that can be of use to an audience like mine, reach out to me.

Anyway, this past weekend, while relocating to Chicago, I had the pleasure of reading Alpha Architect’s (Gray, Vogel, Foulke) book “DIY financial advisor ”, essentially making a case as to why a retail investor should be able to outperform the expert financial advisers that charge several percentage points a year to manage one’s wealth.

The book starts off by citing various famous studies showing how many subtle subconscious biases and fallacies human beings are susceptible to (there are plenty), such as falling for complexity, overconfidence, and so on—none of which emotionless computerized systems and models suffer from. Furthermore, it also goes on to provide several anecdotal examples of experts gone bust, such as Victor Niederhoffer, who blew up not once, but twice (and rumor has it he blew up a third time), and studies showing that systematic data analysis has shown to beat expert recommendations time and again—including when experts were armed with the outputs of the models themselves. Throw in some quotes from Jim Simons (CEO of the best hedge fund in the world, Renaissance Technologies), and the first part of the book can be summed up like this:

1) Your rank and file human beings are susceptible to many subconscious biases.

2) Don’t trust the recommendations of experts. Even simpler models have systematically outperformed said “experts”. Some experts have even blown up, multiple times even (E. G. Victor Niederhoffer).

3) Building an emotionless system will keep these human fallacies from wrecking your investment portfolio.

4) Sticking to a well thought-out system is a good idea, even when it’s uncomfortable—such as when a marine has to wear a Kevlar helmet, hold extra ammo, and extra water in a 126 degree Iraq desert (just ask Dr./Captain Gray!).

This is all well and good—essentially making a very strong case for why you should build a system, and let the system do the investment allocation heavy lifting for you.

Next, the book goes into the FACTS acronym of different manager selection—fees, access, complexity, taxes, and search. Fees: how much does it cost to have someone manage your investments? Pretty self-explanatory here. Access: how often can you pull your capital (EG a hedge fund that locks you up for a year especially when it loses money should be run from, and fast). Complexity: do you understand how the investments are managed? Taxes: long-term capital gains, or shorter-term? Generally, very few decent systems will be holding for a year or more, so in my opinion, expect to pay short-term taxes. Search: that is, how hard is it to find a good candidate? Given the sea of hedge funds (especially those with short-term track records, or only track records managing tiny amounts of money), how hard is it to find a manager who’ll beat the benchmark after fees? Answer: very difficult. In short, all the glitzy sophisticated managers you hear about? Far from a terrific deal, assuming you can even find one.

Continuing, the book goes into two separate anomalies that should form the foundation for any equity investment strategy – value, and momentum. The value system essentially goes long the top decile of the EBIT/TEV metric for the top 60% of market-cap companies traded on the NYSE every year. In my opinion, this is a system that is difficult to implement for the average investor in terms of managing the data process for this system, along with having the proper capital to allocate to all the various companies. That is, if you have a small amount of capital to invest, you might not be able to get that equal weight allocation across a hundred separate companies. However, I believe that with the QVAL and IVAL etfs (from Alpha Architect, and full disclosure, I have some of my IRA invested there), I think that the systematic value component can be readily accessed through these two funds.

The momentum strategy, however, is much simpler. There’s a momentum component, and a moving average component. There’s some math that shows that these two signals are related (a momentum signal is actually proportional to a difference of a moving average and its last value), and the ROBUST system that this book proposes is a combination of a momentum signal and an SMA signal. This is how it works. Assume you have $100,000 and 5 assets to invest in, for the sake of math. Divide the portfolio into a $50,000 momentum component and a $50,000 moving average component. Every month, allocate $10,000 to each of the five assets with a positive 12-month momentum, or stay in cash for that asset. Next, allocate another $10,000 to each of the five assets with a price above a 12-month simple moving average. It’s that simple, and given the recommended ETFs (commodities, bonds, foreign stocks, domestic stocks, real estate), it’s a system that most investors can rather easily implement, especially if they’ve been following my blog.

For those interested in more market anomalies (especially value anomalies), there’s a chapter which contains a large selection of academic papers that go back and forth on the efficacies of various anomalies and how well they can predict returns. So, for those interested, it’s there.

The book concludes with some potential pitfalls which a DIY investor should be aware of when running his or her own investments, which is essentially another psychology chapter.

Overall, in my opinion, this book is fairly solid in terms of reasons why a retail investor should take the plunge and manage his or her own investments in a systematic fashion. Namely, that flesh and blood advisers are prone to human error, and on top of that, usually charge an unjustifiably high fee, and then deliver lackluster performance. The book recommends a couple of simple systems, one of which I think anyone who can copy and paste some rudimentary R can follow (the ROBUST momentum system), and another which I think most stay-at-home investors should shy away from (the value system, simply because of the difficulty of dealing with all that data), and defer to either or both of Alpha Architect’s 2 ETFs.

In terms of momentum, there are the ALFA, GMOM, and MTUM tickers (do your homework, I’m long ALFA) for various differing exposures to this anomaly, for those that don’t want to pay the constant transaction costs/incur short-term taxes of running their own momentum strategy.

In terms of where this book comes up short, here are my two cents:

Tested over nearly a century, the risk-reward tradeoffs of these systems can still be frightening at times. That is, for a system that delivers a CAGR of around 15%, are you still willing to risk a 50% drawdown? Losing half of everything on the cusp of retirement sounds very scary, no matter the long-term upside.

Furthermore, this book keeps things simple, with an intended audience of mom and pop investors (who have historically underperformed the SP 500!). While I think it accomplishes this, I think there could have been value added, even for such individuals, by outlining some ETFs or simple ETF/ETN trading systems that can diversify a portfolio. For instance, while volatility trading sounds very scary, in the context of providing diversification, it may be worth looking into. For instance, 2008 was a banner year for most volatility trading strategies that managed to go long and stay long volatility through the crisis. I myself still have very little knowledge of all of the various exotic ETFs that are popping up left and right, and I would look very favorably on a reputable source that can provide a tour of some that can provide respectable return diversification to a basic equities/fixed-income/real asset ETF-based portfolio, as outlined in one of the chapters in this book, and other books, such as Meb Faber’s Global Asset Allocation (a very cheap ebook) .

One last thing that I’d like to touch on—this book is written in a very accessible style, and even the math (yes, math!) is understandable for someone that’s passed basic algebra. It’s something that can be read in one or two sittings, and I’d recommend it to anyone that’s a beginner in investing or systematic investing.

Overall, I give this book a solid 4/5 stars. It’s simple, easily understood, and brings systematic investing to the masses in a way that many people can replicate at home. However, I would have liked to see some beyond-the-basics content as well given the plethora of different ETFs.

Thanks for reading.

Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

This post will demonstrate a stop-loss rule inspired by Andrew Los paper when do stop-loss rules stop losses. Furthermore, it will demonstrate how to deflate a Sharpe ratio to account for the total number of trials conducted, which is presented in a paper written by David H. Bailey and Marcos Lopez De Prado. Lastly, the strategy will be tested on the out-of-sample ETFs, rather than the mutual funds that have been used up until now (which actually cannot be traded more than once every two months, but have been used simply for the purpose of demonstration).

First, however, Id like to fix some code from the last post and append some results.

A reader asked about displaying the max drawdown for each of the previous rule-testing variants based off of volatility control, and Brian Peterson also recommended displaying max leverage, which this post will provide.

Heres the updated rule backtest code:

Here are the two new plots.

Drawdowns:

Here are the results presented as a hypothesis testa linear regression of drawdowns and leverage against momentum formation period and volatility calculation period:

Easy interpretation herethe shorter-term volatility estimates are unstable due to the one-asset rotation nature of the system. Particularly silly is using the one-month volatility estimate. Imagine the system just switched from the lowest-volatility instrument to the highest. It would then take excessive leverage and get blown up that month for no particularly good reason. A longer-term volatility estimate seems to do much better for this system. So, while the Sharpe is generally improved, the results become far more palatable when using a more stable calculation for volatility, which sets maximum leverage to about 2 when targeting an annualized volatility of 10%. Also, to note, the period to compute volatility matters far more than the momentum formation period when addressing volatility targeting, which lends credence (at least in this case) to so many people that say the individual signal rules matter far less than the position-sizing rules!. According to some, position sizing is often a way for people to mask only marginally effective (read: bad) strategies with a separate layer to create a better result. Im not sure which side of the debate (even assuming there is one) I fall upon, but for what its worth, there it is.

Moving on, I want to test out one more rule, which is inspired by Andrew Los stop-loss rule. Essentially, the way it works is this (to my interpretation): it evaluates a running standard deviation, and if the drawdown exceeds some threshold of the running standard deviation, to sit out for some fixed period of time, and then re-enter. According to Andrew Lo, stop-losses help momentum strategies, so it seems as good a rule to test as any.

However, rather than test different permutations of the stop rule on all 144 prior combinations of volatility-adjusted configurations, Im going to take an ensemble strategy, inspired by a conversation I had with Adam Butler, the CEO of ReSolve Asset Management. who stated that we know momentum exists, but we dont know the perfect way to measure it, from the section I just finished up and use an equal weight of all 12 of the momentum formation periods with a 252-day rolling annualized volatility calculation, and equal weight them every month.

Here are the base case results from that trial (bringing our total to 169).

With the following result:

Of course, also worth nothing is that the annualized standard deviation is indeed very close to 10%, even with the ensemble. And its nice that there is a Sharpe past 1. Of course, given that these are mutual funds being backtested, these results are optimistic due to the unrealistic execution assumptions (cant trade sooner than once every *two* months).

Anyway, lets introduce our stop-loss rule, inspired by Andrew Los paper .

Essentially, the way it works is like this: the function computes all the drawdowns for a return series, along with its running standard deviation (non-annualizedif you want to annualize it, change the sdScaling parameter to something like sqrt(12) for monthly or sqrt(252) for daily data). Next, it looks for when the drawdown crossed a critical threshold, then cuts off that portion of returns and standard deviation history, and moves ahead in history by the cooldown period specified, and repeats. Most of the code is simply dealing with corner cases (is there even a time to use the stop rule? What about iterating when there isnt enough data left?), and then putting the results back together again.

In any case, for the sake of simplicity, this function doesnt use two different time scales (IE compute volatility using daily data, make decisions monthly), so Im sticking with using a 12-month rolling volatility, as opposed to 252 day rolling volatility multiplied by the square root of 21.

Finally, here are another 54 runs to see if Andrew Los stop-loss rule works here. Essentially, the intuition behind this is that if the strategy breaks down, itll continue to break down, so it would be prudent to just turn it off for a little while.

Here are the trial runs:

And a plot of the results.

Result: at this level, and at this frequency (retaining the monthly decision-making process), the stop-loss rule basically does nothing in order to improve the risk-reward trade-off in the best case scenarios, and in most scenarios, simply hurts. 54 trials down the drain, bringing us up to 223 trials. So, what does the final result look like?

Heres the final in-sample equity curveand the first one featured in this entire series. This is, of course, a *feature* of hypothesis-driven development. Playing whack-a-mole with equity curve bumps is what is a textbook case of overfitting. So, without further ado:

And now we can see why stop-loss rules generally didnt add any value to this strategy. Simply, it had very few periods of sustained losses at the monthly frequency, and thus, very little opportunity for a stop-loss rule to add value. Sure, the occasional negative month crept in, but there was no period of sustained losses. Furthermore, Yahoo Finance may not have perfect fidelity on dividends on mutual funds from the late 90s to early 2000s, so the initial flat performance may also be a rather conservative estimate on the strategys performance (then again, as I stated before, using mutual funds themselves is optimistic given the unrealistic execution assumptions, so maybe it cancels out). Now, if this equity curve were to be presented without any context, one may easily question whether or not it was curve-fit. To an extent, one can argue that the volatility computation period may be optimized, though Id hardly call a 252-day (one-year) rolling volatility estimate a curve-fit.

Next, Id like to introduce another concept on this blog that Ive seen colloquially addressed in other parts of the quantitative blogging space, particularly by Mike Harris of Price Action Lab. namely that of multiple hypothesis testing, and about the need to correct for that.

Luckily for that, Drs. David H. Bailey and Marcos Lopez De Prado wrote a paper to address just that. Also, Id like to note one very cool thing about this paper: it actually has a worked-out numerical example! In my opinion, there are very few things as helpful as showing a simple result that transforms a collection of mathematical symbols into a result to demonstrate what those symbols actually mean in the span of one page. Oh, and it also includes *code* in the appendix (albeit Python — even though, you know, R is far more developed. If someone can get Marcos Lopez De Prado to switch to Raka the better research language, thatd be a godsend!).

In any case, heres the formula for the deflated Sharpe ratio, implemented straight from the paper.

The inputs are the strategys Sharpe ratio, the number of backtest runs, the variance of the sharpe ratios of those backtest runs, the skewness of the candidate strategy, its non-excess kurtosis, the number of periods in the backtest, and the number of periods in a year. Unlike the De Prado paper, I choose to return the p-value (EG 1-.

Lets collect all our Sharpe ratios now.

And now, lets plug and chug!

And the result!

Success! At least at the 5% leveland a rejection at the 1% level, and any level beyond that.

So, one last thing! Out-of-sample testing on ETFs (and mutual funds during the ETF burn-in period)!

And the results:

And one more equity curve (only the second!).

In other words, the out-of-sample statistics compare to the in-sample statistics. The Sharpe ratio is higher, the Calmar slightly lower. But on a whole, the performance has kept up. Unfortunately, the strategy is currently in a drawdown, but thats the breaks.

So, whew. That concludes my first go at hypothesis-driven development, and has hopefully at least demonstrated the process to a satisfactory degree. What started off as a toy strategy instead turned from a rejection to a not rejection to demonstrating ideas from three separate papers, and having out-of-sample statistics that largely matched if not outperformed the in-sample statistics. For those thinking about investing in this strategy (again, here is the strategy: take 12 different portfolios, each selecting the asset with the highest momentum over months 1-12, target an annualized volatility of 10%, with volatility defined as the rolling annualized 252-day standard deviation, and equal-weight them every month), what I didnt cover was turnover and taxes (this is a bond ETF strategy, so dividends will play a large role).

Now, one other requestmany of the ideas for this blog come from my readers. I am especially interested in things to think about from readers with line-management responsibilities, as I think many of the questions from those individuals are likely the most universally interesting ones. If youre one such individual, Id appreciate an introduction, and knowing who more of the individuals in my reader base are.

Thanks for reading.

NOTE: while I am currently consulting, I am always open to networking, meeting up, consulting arrangements, and job discussions. Contact me through my email at ilya. kipnisgmail, or through my LinkedIn, found here.

Hypothesis Driven Development Part IV: Testing The Barroso/Santa Clara Rule

This post will deal with applying the constant-volatility procedure written about by Barroso and Santa Clara in their paper Momentum Has Its Moments.

The last two posts dealt with evaluating the intelligence of the signal-generation process. While the strategy showed itself to be marginally better than randomly tossing darts against a dartboard and I was ready to reject it for want of moving onto better topics that are slightly less of a toy in terms of examples than a little rotation strategy, Brian Peterson told me to see this strategy through to the end, including testing out rule processes.

First off, to make a distinction, rules are not signals. Rules are essentially a way to quantify what exactly to do assuming one acts upon a signal. Things such as position sizing, stop-loss processes, and so on, all fall under rule processes.

This rule deals with using leverage in order to target a constant volatility.

So heres the idea: in their paper, Pedro Barroso and Pedro Santa Clara took the Fama-French momentum data, and found that the classic WML strategy certainly outperforms the market, but it has a critical downside, namely that of momentum crashes, in which being on the wrong side of a momentum trade will needlessly expose a portfolio to catastrophically large drawdowns. While this strategy is a long-only strategy (and with fixed-income ETFs, no less), and so would seem to be more robust against such massive drawdowns, theres no reason to leave money on the table. To note, not only have Barroso and Santa Clara covered this phenomena, but so have others, such as Tony Cooper in his paper Alpha Generation and Risk Smoothing Using Volatility of Volatility.

In any case, the setup here is simple: take the previous portfolios, consisting of 1-12 month momentum formation periods, and every month, compute the annualized standard deviation, using a 21-252 (by 21) formation period, for a total of 12 x 12 = 144 trials. (So this will put the total trials run so far at 24 + 144 = 168bonus points if you know where this tidbit is going to go).

Heres the code (again, following on from the last post. which follows from the second post. which follows from the first post in this series).

Again, theres no parallel code since this is a relatively small example, and I dont know which OS any given instance of R runs on (windows/linux have different parallelization infrastructure).

So the idea here is to simply compare the Sharpe ratios with different volatility lookback periods against the baseline signal-process-only portfolios. The reason I use Sharpe ratios, and not say, CAGR, volatility, or drawdown is that Sharpe ratios are scale-invariant. In this case, Im targeting an annualized volatility of 10%, but with a higher targeted volatility, one can obtain higher returns at the cost of higher drawdowns, or be more conservative. But the Sharpe ratio should stay relatively consistent within reasonable bounds.

So here are the results:

Sharpe improvements:

In this case, the diagram shows that on a whole, once the volatility estimation period becomes long enough, the results are generally positive. Namely, that if one uses a very short estimation period, that volatility estimate is more dependent on the last months choice of instrument, as opposed to the longer-term volatility of the system itself, which can create poor forecasts. Also to note is that the one-month momentum formation period doesnt seem overly amenable to the constant volatility targeting scheme (theres basically little improvement if not a slight drag on risk-adjusted performance). This is interesting in that the baseline Sharpe ratio for the one-period formation is among the best of the baseline performances. However, on a whole, the volatility targeting actually does improve risk-adjusted performance of the system, even one as simple as throwing all your money into one asset every month based on a single momentum signal.

Absolute Sharpe ratios:

In this case, the absolute Sharpe ratios look fairly solid for such a simple system. The 3, 7, and 9 month variants are slightly lower, but once the volatility estimation period reaches between 126 and 252 days, the results are fairly robust. The Barroso and Santa Clara paper uses a period of 126 days to estimate annualized volatility, which looks solid across the entire momentum formation period spectrum.

In any case, it seems the verdict is that a constant volatility target improves results.

Thanks for reading.

NOTE: while I am currently consulting, I am always open to networking, meeting up (Philadelphia and New York City both work), consulting arrangements, and job discussions. Contact me through my email at ilya. kipnisgmail, or through my LinkedIn, found here .

Hypothesis Driven Development Part III: Monte Carlo In Asset Allocation Tests

This post will show how to use Monte Carlo to test for signal intelligence.

Although I had rejected this strategy in the last post. I was asked to do a monte-carlo analysis of a thousand random portfolios to see how the various signal processes performed against said distribution. Essentially, the process is quite simple: as Im selecting one asset each month to hold, I simply generate a random number between 1 and the amount of assets (5 in this case), and hold it for the month. Repeat this process for the number of months, and then repeat this process a thousand times, and see where the signal processes fall across that distribution.

I didnt use parallel processing here since Windows and Linux-based R have different parallel libraries, and in the interest of having the code work across all machines, I decided to leave it off.

Heres the code:

And here are the results:

In short, compared to monkeys throwing darts, to use some phrasing from the Price Action Lab blog. these signal processes are only marginally intelligent, if at all, depending on the variation one chooses. Still, I was recommended to see this process through the end, and evaluate rules, so next time, Ill evaluate one easy-to-implement rule.

Thanks for reading.

NOTE: while I am currently consulting, I am always open to networking, meeting up (Philadelphia and New York City both work), consulting arrangements, and job discussions. Contact me through my email at ilya. kipnisgmail, or through my LinkedIn, found here .

Hypothesis-Driven Development Part II

This post will evaluate signals based on the rank regression hypotheses covered in the last post.

The last time around, we saw that rank regression had a very statistically significant result. Therefore, the next step would be to evaluate the basic signals — whether or not there is statistical significance in the actual evaluation of the signalnamely, since the strategy from SeekingAlpha simply selects the top-ranked ETF every month, this is a very easy signal to evaluate.

Simply, using the 1-24 month formation periods for cumulative sum of monthly returns, select the highest-ranked ETF and hold it for one month.

Heres the code to evaluate the signal (continued from the last post), given the returns, a month parameter, and an EW portfolio to compare with the signal.

Okay, so whats going on here is that I compare the signal against the equal weight portfolio, and take means and z scores of both the signal values in general, and against the equal weight portfolio. I plot these values, along with boxplots of the distributions of both the signal process, and the difference between the signal process and the equal weight portfolio.

Here are the results:

To note, the percents are already multiplied by 100, so in the best cases, the rank strategy outperforms the equal weight strategy by about 30 basis points per month. However, these results arenot even in the same parking lot as statistical significance, let alone in the same ballpark.

Now, at this point, in case some people havent yet read Brian Petersons paper on strategy development. the point of hypothesis-driven development is to *reject* hypothetical strategies ASAP before looking at any sort of equity curve and trying to do away with periods of underperformance. So, at this point, I would like to reject this entire strategy because theres no statistical evidence to actually continue. Furthermore, because August 2015 was a rather interesting month, especially in terms of volatility dispersion, I want to return to volatility trading strategies, now backed by hypothesis-driven development.

If anyone wants to see me continue to rule testing with this process, let me know. If not, I have more ideas on the way.

Thanks for reading.

NOTE: while I am currently consulting, I am always open to networking, meeting up (Philadelphia and New York City both work), consulting arrangements, and job discussions. Contact me through my email at ilya. kipnisgmail, or through my LinkedIn, found here.

Introduction to Hypothesis Driven Development — Overview of a Simple Strategy and Indicator Hypotheses

This post will begin to apply a hypothesis-driven development framework (that is, the framework written by Brian Peterson on how to do strategy construction correctly, found here ) to a strategy Ive come across on SeekingAlpha. Namely, Cliff Smith posted about a conservative bond rotation strategy. which makes use of short-term treasuries, long-term treasuries, convertibles, emerging market debt, and high-yield corporate debtthat is, SHY, TLT, CWB, PCY, and JNK. What this post will do is try to put a more formal framework on whether or not this strategy is a valid one to begin with.

One note: For the sake of balancing succinctness for blog consumption and to demonstrate the computational techniques more quickly, Ill be glossing over background research write-ups for this post/strategy, since its yet another take on time-series/cross-sectional momentum, except pared down to something more implementable for individual investors, as opposed to something that requires a massive collection of different instruments for massive, institutional-class portfolios.

Introduction, Overview, Objectives, Constraints, Assumptions, and Hypotheses to be Tested:

Momentum. It has been documented many times. For the sake of brevity, Ill let readers follow the links if theyre so inclined, but among them are Jegadeesh and Titmans seminal 1993 paper, Mark Carharts 1997 paper, Andreu et. Al (2012), Barroso and Santa-Clara (2013), Ilmanens Expected Returns (which covers momentum), and others. This list, of course, is far from exhaustive, but the point stands. Formation periods of several months (up to a year) should predict returns moving forward on some holding period, be it several months, or as is more commonly seen, one month.

Furthermore, momentum applies in two varietiescross sectional, and time-series. Cross-sectional momentum asserts that assets that outperformed among a group will continue to outperform, while time-series momentum asserts that assets that have risen in price during a formation period will continue to do so for the short-term future.

Cliff Smiths strategy depends on the latter, effectively, among a group of five bond ETFs. I am not certain of the objective of the strategy (he didnt mention it), as PCY, JNK, and CWB, while they may be fixed-income in name, possess volatility on the order of equities. I suppose one possible default objective would be to achieve an outperforming total return against an equal-weighted benchmark, both rebalanced monthly.

The constraints are that one would need a sufficient amount of capital such that fixed transaction costs are negligible, since the strategy is a single-instrument rotation type, meaning that each month may have two-way turnover of 200% (sell one ETF, buy another). On the other hand, one would assume that the amount of capital deployed is small enough such that execution costs of trading do not materially impact the performance of the strategy. That is to say, moving multiple billions from one of these ETFs to the other is a non-starter. As all returns are computed close-to-close for the sake of simplicity, this creates the implicit assumption that the market impact and execution costs are very small compared to overall returns.

There are two overarching hypotheses to be tested in order to validate the efficacy of this strategy:

1) Time-series momentum: while it has been documented for equities and even industry/country ETFs, it may not have been formally done so yet for fixed-income ETFs, and their corresponding mutual funds. In order to validate this strategy, it should be investigated if the particular instruments it selects adhere to the same phenomena.

2) Cross-sectional momentum: again, while this has been heavily demonstrated in the past with regards to equities, ETFs are fairly new, and of the five mutual funds Cliff Smith selected, the latest one only has data going back to 1997, thus allowing less sophisticated investors to easily access diversified fixed income markets a relatively new innovation.

Essentially, both of these can be tested over a range of parameters (1-24 months).

Another note: with hypothesis-driven strategy development, the backtest is to be *nothing more than a confirmation of all the hypotheses up to that point*. That is, re-optimizing on the backtest itself means overfitting. Any proposed change to a strategy should be done in the form of tested hypotheses, as opposed to running a bunch of backtests and selecting the best trials. Taken another way, this means that every single proposed element of a strategy needs to have some form of strong hypothesis accompanying it, in order to be justified.

So, here are the two hypotheses I tested on the corresponding mutual funds:

Essentially, in this case, I take a pooled regression (that is, take the five instruments and pool them together into one giant vector), and regress the cumulative sum of monthly returns against the next months return. Also, I do the same thing as the above, except also using cross-sectional ranks for each month, and performing a rank-rank regression. The sample I used was the five mutual funds (CNSAX, FAHDX, VUSTX, VFISX, and PREMX) since their inception to March 2009, since the data for the final ETF begins in April of 2009, so I set aside the ETF data for out-of-sample backtesting.

Here are the results:

Of interest to note is that while much of the momentum literature specifies a reversion effect on time-series momentum at 12 months or greater, all the regression coefficients in this case (even up to 24 months!) proved to be positive, with the very long-term coefficients possessing more statistical significance than the short-term ones. Nevertheless, Cliff Smiths chosen parameters (the two and four month settings) possess statistical significance at least at the 10% level. However, if one were to be highly conservative in terms of rejecting strategies, that in and of itself may be reason enough to reject this strategy right here.

However, the rank-rank regression (that is, regressing the future months cross-sectional rank on the past n month sum cross sectional rank) proved to be statistically significant beyond any doubt, with all p-values being effectively zero. In short, there is extremely strong evidence for cross-sectional momentum among these five assets, which extends out to at least two years. Furthermore, since SHY or VFISX, aka the short-term treasury fund, is among the assets chosen, since its a proxy for the risk-free rate, by including it among the cross-sectional rankings, the cross-sectional rankings also implicitly state that in order to be invested into (as this strategy is a top-1 asset rotation strategy), it must outperform the risk-free asset, otherwise, by process of elimination, the strategy will invest into the risk-free asset itself.

In upcoming posts, Ill look into testing hypotheses on signals and rules.

Lastly, Volatility Made Simple has just released a blog post on the performance of volatility-based strategies for the month of August. Given the massive volatility spike, the dispersion in performance of strategies is quite interesting. Im happy that in terms of YTD returns, the modified version of my strategy is among the top 10 for the year.

Thanks for reading.

NOTE: while I am currently consulting, I am always open to networking, meeting up (Philadelphia and New York City both work), consulting arrangements, and job discussions. Contact me through my email at ilya. kipnisgmail, or through my LinkedIn, found here .

Review: Invoances TRAIDE application

This review will be about Inovance Techs TRAIDE system. It is an application geared towards letting retail investors apply proprietary machine learning algorithms to assist them in creating systematic trading strategies. Currently, my one-line review is that while I hope the company founders mean well, the application is still in an early stage, and so, should be checked out by potential users/venture capitalists as something with proof of potential, rather than a finished product ready for mass market. While this acts as a review, its also my thoughts as to how Inovance Tech can improve its product.

A bit of background: I have spoken several times to some of the companys founders, who sound like individuals at about my age level (so, fellow millennials). Ultimately, the selling point is this:

Systematic trading is cool.

Machine learning is cool.

Therefore, applying machine learning to systematic trading is awesome! (And a surefire way to make profits, as Renaissance Technologies has shown.)

While this may sound a bit snarky, its also, in some ways, true. Machine learning has become the talk of the town, from IBMs Watson (RenTec itself hired a bunch of speech recognition experts from IBM a couple of decades back), to Stanfords self-driving car (invented by Sebastian Thrun, who now heads Udacity), to the Netflix prize, to god knows what Andrew Ng is doing with deep learning at Baidu. Considering how well machine learning has done at much more complex tasks than create a half-decent systematic trading algorithm, it shouldnt be too much to ask this powerful field at the intersection of computer science and statistics to help the retail investor glued to watching charts generate a lot more return on his or her investments than through discretionary chart-watching and noise trading. To my understanding from conversations with Inovance Techs founders, this is explicitly their mission.

However, I am not sure that Inovances TRAIDE application actually accomplishes this mission in its current state.

Heres how it works:

Users select one asset at a time, and select a date range (data going back to Dec. 31, 2009). Assets are currently limited to highly liquid currency pairs, and can take the following settings: 1 hour, 2 hour, 4 hour, 6 hour, or daily bar time frames.

Users then select from a variety of indicators, ranging from technical (moving averages, oscillators, volume calculations, etc. Mostly an assortment of 20th century indicators, though the occasional adaptive moving average has managed to sneak innamely KAMAsee my DSTrading package, and MAMAaka the Mesa Adaptive Moving Average, from John Ehlers) to more esoteric ones such as some sentiment indicators. Heres where things start to head south for me, however. Namely, that while its easy to add as many indicators as a user would like, there is basically no documentation on any of them, with no links to reference, etc. so users will have to bear the onus of actually understanding what each and every one of the indicators they select actually does, and whether or not those indicators are useful. The TRAIDE application makes zero effort (thus far) to actually get users acquainted with the purpose of these indicators, what their theoretical objective is (measure conviction in a trend, detect a trend, oscillator type indicator, etc.)

Furthermore, regarding indicator selections, users also specify one parameter setting for each indicator per strategy. E. G. if I had an EMA crossover, Id have to create a new strategy for a 20/100 crossover, a 21/100 crossover, rather than specifying something like this:

short EMA: 20-60

long EMA: 80-200

Quantstrat itself has this functionality, and while I dont recall covering parameter robustness checks/optimization (in other words, testing multiple parameter setswhether one uses them for optimization or robustness is up to the user, not the functionality) in quantstrat on this blog specifically, this information very much exists in what I deem the official quantstrat manual, found here. In my opinion, the option of covering a range of values is mandatory so as to demonstrate that any given parameter setting is not a random fluke. Outside of quantstrat, I have demonstrated this methodology in my Hypothesis Driven Development posts, and in coming up for parameter selection for volatility trading.

Where TRAIDE may do something interesting, however, is that after the user specifies his indicators and parameters, its proprietary machine learning algorithms (WARNING: COMPLETELY BLACK BOX) determine for what range of values of the indicators in question generated the best results within the backtest, and assign them bullishness and bearishness scores. In other words, looking backwards, these were the indicator values that did best over the course of the sample. While there is definite value to exploring the relationships between indicators and future returns, I think that TRAIDE needs to do more in this area, such as reporting P-values, conviction, and so on.

For instance, if you combine enough indicators, your rule is a market order thats simply the intersection of all of the ranges of your indicators. For instance, TRAIDE may tell a user that the strongest bullish signal when the difference of the moving averages is between 1 and 2, the ADX is between 20 and 25, the ATR is between 0.5 and 1, and so on. Each setting the user selects further narrows down the number of trades the simulation makes. In my opinion, there are more ways to explore the interplay of indicators than simply one giant AND statement, such as an OR statement, of some sort. (E. G. select all values, put on a trade when 3 out of 5 indicators fall into the selected bullish range in order to place more trades). While it may be wise to filter down trades to very rare instances if trading a massive amount of instruments, such that of several thousand possible instruments, only several are trading at any given time, with TRAIDE, a user selects only *one* asset class (currently, one currency pair) at a time, so Im hoping to see TRAIDE create more functionality in terms of what constitutes a trading rule.

After the user selects both a long and a short rule (by simply filtering on indicator ranges that TRAIDEs machine learning algorithms have said are good), TRAIDE turns that into a backtest with a long equity curve, short equ

Professional forex trading strategies best binary option brokers

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Short term trading strategies that work amazon

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7do’s and don’ts for rookie investors

7do’s and don’ts for rookie investors7 Do’s and Don’ts for Rookie Investors

Just because you're a beginner doesn't mean you have to invest like one.

Taking the initial plunge into investing can be a nerve-racking move. What should you buy? Who can you trust? And most harrowing of all: What if you end up losing money?

At its core, the concept of investing is simple. The problem is investors are often tripped up by emotion, speculation and poor advice from others.

Investing your money is the key to reaching long-term savings goals, and it doesn't have to be a stressful or arduous process. Follow these do’s and don'ts to start your venture into investing off right.

1. Do: A ton of research.

Before locking your hard-earned dollars into a stock, mutual fund, exchange-traded fund or other market investment, be sure it's worthy of your money. Television shows, radio programs, articles and other sources of investment advice can point you in the right direction, but should serve only as a starting place. Let us remember “Mad Money” host Jim Cramer's 2008 Bear Stearns statement, if you disagree.

This goes for the person you choose to help manage your investments as well. You don't have to go at it alone, but the same amount of careful research you put into choosing your first investments should also be applied to choosing a financial advisor if and when you're ready to hire one.

2. Don't: Try to time the market.

You can't.

As the most famous and well-respected investor of all, Warren Buffett, wrote in this year’s letter to Berkshire Hathaway shareholders, The goal of the nonprofessional should not be to pick winners – neither he nor his ‘helpers’ can do that – but should rather be to own a cross section of businesses that in aggregate are bound to do well. (He recommends Vanguard's 500 Index in case you're wondering.)

3. Do: Diversify your investments.

Too often, novice investors mistakenly equate the concept of diversification with owning many investments. However, a diversified portfolio should not be varied only in number of investments, but types of investments, too.

For instance, a portfolio of only stocks would be too aggressive even for a 20-year-old. A tech-heavy collection of investments would make your nest egg far too reliant on the performance of one industry.

This goes for investing in company stock, too. Considering that you are already dependent on your employer for your livelihood – salary, benefits, perhaps a 401(k) match – it's safe to say you're already overly-invested in it. Allocate a significant portion of your portfolio to that same company, and you become dangerously dependent on that one investment to pan out. One word: Enron.

4. Don't: Invest according to emotion.

When the market crashed in 2008, I was working for a financial planner who had about 100 high-net worth clients. Those clients watched the value of their portfolios get cut in half within just a few short days, and most did what any normal person would: They panicked. They wanted to pull their money out before any more of it disappeared.

Those who waited out the storm at the advice of their advisor regained all the money they lost, and then some. Those who bailed when the market was at its lowest took huge losses they might never be able to make up.

Positive emotions can be just as destructive to your earnings; don't fall in love with an investment that isn't performing with the hopes that it will regain value some day in the future. There's no place for love or fear in investing, only calculated decisions based on data.

5. Do: Pay very close attention to fees.

On paper, your returns for the year might seem impressive. Unfortunately, once you subtract all the fees paid to purchase and manage those investments, the yield starts to look less exciting. In fact, you could easily end up sacrificing 40 percent of your return to fees, according to Forbes.

Trade commissions, expense ratios, advisor fees – they all add up and take a big bite out of earnings. It pays, literally, to research the costs associated with all possible investments before making a final decision.

Think you can make up for lost time by investing more money down the road? Thanks to compound interest, you would still earn more over the life of your investments by investing less now than a bigger chunk later. Add inflation and increasing life spans to the equation, and you can't afford to give that money up to procrastination.

7. Do: Maintain cash savings.

Finally, it's very important to understand the objectives of your savings and how to put it to work based on your goals.

Market investments are for the long-term – money you won't need for at least 10 years, ideally longer. Retirement is a perfect example. But what about today?

A separate, cash savings account should always be maintained for immediate needs. After all, because of the time, fees and future earnings sacrificed, your least desirable option for covering a new transmission or roof repair would be to sell off shares of an investment.

You never want your emergency savings to be tied up. Keeping a minimum of $10,000 in any combination of savings accounts and short-term certificates of deposit should protect you from surprise expenses that need to be covered immediately.

Forex00levels

Forex00levelsLatest news

Lucky 5 Strategy: Trading 00 Levels

Details Published: 03.10.2014 15:58 Written by Admin Category: Trading strategies Hits: 955

Prices in the Forex market are formed by the actual supply and demand. Efficient Lucky 5 strategy on a grid of pending orders uses the strength of "00" price levels, based on the logic and psychology of crowd behavior.

Naturally, men want to simplify everything, and the financial market is no exception. Mass thinking of the players always rounds prices and reaches for the key time points. This allowed to create a whole family of psychological trading techniques, and the Lucky 5 strategy is one of them.

Levels of elevated price activity (danger)

00 price levels refer to levels that are multiples of 100. This means the price of the *.**00 kind (1.3500, 1.6100, 0.9200) – the price with two zeros on the end in the case of 4-digit quotes, or ***.00 (138.00, 140.00) for the 2-digit quotes of the yen pairs, or in case of the 5-digit quotation – (*.**000) and 3-digit (***.000) in pairs with the yen.

Such basic price levels are the most attractive from the point of view of human psychology. They become the battleground for bulls and bears, regrouping and reallocation of resources often occurs there.

The fact that the market is almost always fighting off the "00" price level means that it is at this price the buyers/sellers establish a temporary balance. Round figure price levels are also an obstacle to rapid news movements and often act as strong support/resistance.

As a rule, prices at which the large foreign exchange or options are set are "00" levels. By the time of expiry (closing), the optional price of the instrument tends to catch up with the price of the spot market, so the presence of large options causes speculative price jumps in the area of basic prices.

Large marketmakers also take this information into account and when prices approach the "00" level, the volumes and number of transactions from their side increase sharply. They create artificial protection at 00 levels, and to enable a financial instrument to overcome such a price, you need not only strong fundamentals, but also the psychological desire of market participants to move to other trading levels.

Lucky 5 strategy: characteristics of the trading system

This technique is based on working out the actual trading impulses on the basic price levels with simultaneous filtering them on higher timeframes, using small trailing stop to transfer transactions to breakeven with a minimum profit.

Frequent loss is the main reason for the beginner’s panic. Triggering 2-3 stop-losses in a row is enough for a novice trader to get disappointed in their strategy and go in search of a new “grail”. The Lucky 5 strategy allows if not eliminate then at least minimize the stops triggering in trading and gives you up to 70% of profitable deals.

Basic conditions of Lucky 5 strategy

Trading terminal: Metatrader 4 (was also tested on cTrader).

Currency pairs: any pairs with high ADR (AverageDailyRange or the average daily price range): EUR/JPY, GBP/JPY, GBR/USD.

Trading period: Frankfurt, London and the beginning of the American session (no more than two hours after the New York opening).

Broker with fast execution of orders and the possibility of trailing from 1 point.

The method of the trading system operation

Every hour we set pending orders on price levels: *.00, *.10, *.20, *.30, *.40,*.50, *.60, *.70, *.80, *.90. We get approximately the following picture:

We trade only if the direction of the last hourly candle (H1) coincides with the direction of the daily candle (D1). Open orders strictly in the direction of the candle.

Setting: take-profit – 5 points, stop-loss – 10 points. When the first order is triggered, we manually transfer the order to breakeven after 3.5 - 4 points of profit. Only trailing stop works further. No additional indicators are commonly used.

The author of the strategy recommends to stop trading at getting 10 points of profit, but this is up to the trader.

Important recommendations when trading under Lucky 5 strategy

At the time of Frankfurt opening, set only *.40 *.50 *.60 levels.

Never sell at *.10 and never buy at *.90.

When trading, monitor ADR: there should be a distance of more than 10 points to the borders of the price range.

If the price has already passed the boundary of the average daily range, no more deals are opened that day.

We trade only on the first intersection of the level.

Sometimes, the template of the Lucky 5 strategy comes with an additional "basement" indicator ASH (AbsoluteStrengthHistogram), which acts as an additional filter. Trading orders should be set if the color of the indicator bar chart is the same as that of the daily and hourly candle. ASH readings help ride out the possible temporary rollbacks of the price if the order was triggered before the deal was transferred to breakeven.

Indicator bar chart allows to accurately identify the prevailing direction.

How to work at price levels of elevated danger

It should be understood that the levels of activity and accumulation of mass are visible to market makers, so often the price a little, only by 1-2 points, fails to reach the "00" level and reverses. In addition, requotes at the levels with lots of orders often lead to serious losses.

Therefore, it is recommended to put pending orders at the price 1-2 points above / below the "00" level.

Levels *.20,*.50,*.80 are also considered the levels of elevated activity (and danger) for intraday trading. Psychologically, a person perceives *.50 level as half way between adjacent baselines. Accordingly, the levels of *.20 and *.80 are perceived as marginal values for fracture toward a certain level, so-called "two steps to a new target."

Advantages of the trading system

Simple trading logic, comprehensible even to a beginner.

Lack of delayed signals and a plurality of additional indicators.

No need of constant monitoring: market analysis takes a few minutes per hour.

Dallas forex traders association january13,2007-powerpoint ppt presentation

Dallas forex traders association january13,2007-powerpoint ppt presentationDallas Forex Traders Association January 13, 2007 - PowerPoint PPT Presentation

PPT Dallas Forex Traders Association January 13, 2007 PowerPoint presentation | free to download - id: 3ffbfd-MDljN

Dallas Forex Traders Association January 13, 2007

Dallas Forex Traders Association January 13, 2007 Carry Trades. you should begin paper trading a demo account The preferred broker is CFOSFX. PowerPoint PPT presentation

Title: Dallas Forex Traders Association January 13, 2007

Dallas Forex Traders AssociationJanuary 13, 2007

Carry Trades

Parallel/Inverse Relationships

Driver/Passenger

Currency Options

Options Strategies

Brokers Platforms

Homework and Training

CARRY TRADES

Long term spot positions which generate income

via currency interest rate differentials.

Interest is paid daily on your open positions and

is generally referred to as swap or rollover.

Swap is tripled on Wednesdays to compensate for

weekend market closure.

Interest is deposited into account daily, but you

do not collect until you close the trade.

Disadvantages (1) Ties up trade capital over

long periods of time (2) Must endure large

fluctuations in spot price to stay in the trade

Advantages (1) Passive trade style (2) Long term

capital appreciation

Carry trading is your currency 401K

Carry Trade Example

BUY 1 standard lot of GBP/JPY (you now own 100K)

Swap 20.55/day

365 x 20.55 7,500.75 per year (from a 1K

investment)

ROI750

If you wish to seriously carry trade, you may

want to consider using the max leverage offered

by the broker (4001). This allows you to control

more of the currency with a smaller investment

and collect more interest.

4001 leverage allows you to control the same

100K with only 250 .

Example (50 mini) x (400) 20K 20K x 5

minis100K 5 mini lots is 250 investment to

control 1 standard lot

parallel/inverse pairs to confirm your trade

They are probably moving in tandem and you could

find another trade

One could be leading and you could plan on an

entry on the lagging pair

Sometimes the move starts with exotics, then is

Inverse/Parallel Matrix

Passenger or Driver.

Whos Driving?

Fresh crosses are driver changes

Which side of the pair is causing the movement?

How do we know? Look at parallel/inverse pairs

Currency Options

Overview

Definitions

Buyer or Seller?

Strategies

Vanilla Calls/Puts

Straddles

Strangles

Covered Calls/Puts

Protective Calls/Puts

Demo Accounts

Resources

Summary/Conclusion

Definitions

Contract

Strike Price

Expiration

Premium

Wheres the money?

Intrinsic Value

Contract

The unit of trade for an option

Usually 1 standard lot (100,000)

Mini-lots can also be traded by selecting

Strike Price

The specified price at which the contract may be

exercised.

Expiration

The date and time at which an option expires and

becomes worthless if not exercised.

On US-style options, you can exercise the option

any time before the expiration

On European-style options, you cannot exercise

the option prior to the expiration

Premium

The amount that the buyer of an option pays to

the seller.

The cost of a premium is based on

Interest rates

Time until expiration

Distance from strike price

Volatility

BUYER or SELLER. A HUGE Difference

If you are the OPTION BUYER

Buying an option contract gives the holder the

RIGHT to buy (call option) or sell (put option),

(but NOT the obligation) an underlying currency

from the writer/seller of the option, at a

specified price (strike price) up to a specified

date (expiration).

Buyers have a limited downside (premium) and a

potentially unlimited upside.

If the option expires out of the money, you pay

only the premium

If the option expires in the money, you receive

the intrinsic value of the option.

If you are the OPTION SELLER

The seller of an option contract HAS THE

OBLIGATION to fulfill the terms of the contract

by delivering the currency to the buyer at a

specified price (strike price).

Sellers have a limited upside (the premium) and a

potentially unlimited downside

Sellers are immediately paid the premium (i. e.

not at expiration)

Sellers receive premiums to offset their risk

If the option expires out of the money, the

seller keeps the premium

If the option expires in the money, the seller

must pay the intrinsic value of the position

(loss), but still keeps the premium

Vanilla Calls(a. k.a. uncovered/naked)

If you buy one call option on the EUR/USD you

control 1 lot.

Current price is 1.3200, strike price is 1.3250,

expires Friday at 1600, premium is 300.

If the market price moves above the strike price

you are in the money and own the currency at

1.3250

If the market price never moves above 1.3250 you

are out of the money and the option expires

Vanilla Puts(a. k.a. uncovered/naked)

If you buy one put option on the EUR/USD you

control 1 lot.

Current price is 1.3200, strike price is 1.3150,

expires Friday at 1600, premium is 300.

If the market price moves below the strike price

you are in the money and own the currency at

Straddles

Buy 1 call option AND 1 put option at the same

strike price and expiration

Current price is 1.2800, Strike price is 1.2800,

expiration is 7 days, premium is 500.

Total out of pocket is 1000 (500 for calls,

it to move about 75 pips to make a reasonable

profit (30).

If price moves up, you make money on the calls

and lose the premium on the puts

If the price moves down, you make money on the

puts and lose the premium on the calls

Strangles

Similar to a straddle but you buy the call and

put options slightly out of the money.

Current price is 1.2800, buy a call at 1.2820 and

a put at 1.2780

Benefit is lower premium

Consider Straddles/Strangles when.

Upcoming volatile news events (NFP)

Buy calls/puts approximately 48 hours prior to

the event with an expiration on the day of the

event. Close the position roughly 2 hours after

the announcement (regardless of outcome)

OR

No trend exists (range bound)

Price is within the overall trading range

(preferably in the middle)

Short Straddle

The GBP/JPY is range bound and you expect it to

stay there for at least one week. Charts are

choppy and hard to read.

The pair is ranging between 223.00 and 224.00

SELL calls just above 224.00 and SELL puts just

below 223.00

If the pair remains in the range, you receive

both premiums

WARNING You must monitor the price with alarms

and if the pair breaks out of the range you must

take corrective action

Covered Calls/Puts

You bought a spot position in the GBP/USD at

1.9225 and it moved to 1.9300 (750). It is now

encountering strong resistance

SELL a call option at 1.9325 and collect the

premium (600)

If the pair continues to move up, you CANNOT

continue to participate in the spot position, but

still keep the call premium. Your profit is 1350

(750 spot 600 premium).

WARNING Some corrective action will be required

to limit loss on the call

If the pair fails at 1.9300 and retraces to

1.9240 (-60 pips), you have still protected your

previous 750 profit (150 spot 600

premium750)

You can use the same strategy with covered puts

Protective Puts/Calls

You own a long spot position in the USD/JPY and

have a large profit you wish to protect

There is an upcoming news announcement that could

drive the pair down

BUY one put option to protect your position

If the news drives the pair down, you exercise

the put and close the spot

If the news drives the pair up, you continue to

profit from the spot position and only lose the

cfosfx/fxew. htm and download the spot

platform (IKON) and option platform (CORE Options

Resources

Please contact CFOSFX directly with platform

questions

If you have strategy questions, please join us in

the PalTalk classroom (superlights) on Wednesdays

from 730-830 p. m.

Summary/Conclusions

Now we can trade

Spot positions when the market is trending

Options when we arent sure what the market is

going to do, we want to protect other profitable

positions or we want to effectively hedge a

position (NFP)

Carry trades for long term capital growth

Brokers and Platforms

Carry trades InterbankFx (Interbankfx)

Spot trades (majors) Gain Capital (forex)

FREE e-signal and charting

Low spreads on majors

Spot trades (exotics) EFX Group (efxgroup)

Low spreads on exotics

SpotOptions CFOS/FX (csfosfx)

IKON platform for spot trades

Core platform for currency options

Training and Homework

Weekly Learning Opportunities

Every Tuesday/Thursday 730-830 p. m.

Currency Analysis with Wes Betters

Every Wednesday 730-830 p. m.

Forex Q/A open forum with Mark McDonnell

Every Wednesday 830-930 p. m.

Big Lights 101 with Jeannette Watson

Location Paltalk classroom (superlights)

Useful Links

blforex (Sean Park)

Numerous Big Lights resources and useful links

http//blackcrest/BigLights_docs. html

(Jeannette Watson)

Archived Options and weekly open forum calls

forexearlywarning (Mark McDonnell)

Daily trade plans

http//finance. groups. yahoo/group/4xsuperlight

s/

Big Lights message board

Have a great weekend!

MMMmmmmm, Beer!

Forex weekly forecast us dollar surges,but for how much longer

Forex weekly forecast us dollar surges,but for how much longerForex Weekly Forecast: US Dollar Surges, but for How Much Longer?

The US Dollar surged on a big jump in yields, but appetite for further USD buying is fading given already one-sided positions. Does it have anything left for the coming week?

The US Dollar finished the week near decade-plus highs versus the Euro as it rallied against nearly all G10 counterparts.

The ECB cant risk being too positive about recent economic developments, otherwise it risks upsetting capital markets mainly bond and equity investors.

The Australian Dollar appears vulnerable to renewed weakness as soft jobs data and a slump in Chinese GDP growth stoke RBA interest rate cut speculation.

Sign up for a free trial of DailyFX-Plus to have access to Trading QA's, educational webinars, updated speculative positioning measures, trading signals and much more!

Want to develop a more in-depth knowledge on the market and strategies? Check out the DailyFX Trading Guides we have produced on a range of topics.

DailyFX provides forex news and technical analysis on the trends that influence the global currency markets.

Learn forex trading with a free practice account and trading charts from FXCM.

Trading strategy test

Trading strategy testTest this trading strategy!

Discipline in the financial markets is the main clue for achieving success and that is why many experts created so many trading strategies and systems with specific rules that should never be infracted once they proved their accuracy.

1) Simple moving averages 7 and 15

1) When SMA 7 crosses over SMA 15.

2) RSI should support the first signal-moving averages crossover - after achieving a breakout above or below the captured trend line

The images below explain the strategy and the projected targets along with the place where you should put your stop loss in both cases; bullish and bearish market.

Take Profit . When one the above-mentioned signs reverse, we should close our position.

Stop loss . The support that resides beyond the entry point in bullish case and of course the next resistance in bearish case.

Dear trader, do not hesitate to put a stop loss; a samurai equally accepts gains and losses before entering the battle because he depends on his sword. You sword is “Discipline” and I hope you can trade like a samurai!

Cumulative Return in Trading Strategy Test

I have a Signal based on order imbalance that I want to test against historic stock data.

I also have the price for each of these Signals and then I calculate a Trend on the price to see if the returns are rising or falling in the last previous 4 prices. From the Signal I have taken

Now my idea is to buy/short each time nSignal rises above 0.70. If the trend is positive then I would place a buy and if it the trend is negative I would place a short order.

I am then getting out of the position when the nSignal begins to decline again. I do not want to have more than one open position at any time. So if there is a Buy/Sell signal occuring while I have an open position I would ignore.

My questions are regarding the coding of the Sell and Buy vectors and calculation of the returns on these. I would ideally like to have 1 vector as output.

I can produce the Buy / Sell signal, but I am stuck on telling R to ignore further buy / sell until nSignal drops and the position is released. I have attached what I would like to calculate.

TradeStation Platform

Walk-Forward Optimizer

Test Your Trading System On Seen and Unseen Data

Many trading systems fail in the real world because they are built and tested on the same set of historical data.

TradeStation's new Walk Forward Optimizer aids in the mitigation of this problem by performing a set of "walk-forward" performance tests against as-yet-unseen market data, thereby simulating the unpredictability of trading a strategy under real market conditions.

Starting where other optimizing methods typically end, the Walk-Forward Optimizer produces an easy-to-understand, pass-fail analysis of a strategy's key performance criteria.

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The Rockwell Trading Club

"60 Minute Trading Courses" Every Friday -

New Strategies, Techniques, Tips Every Week

"The only constant is change"

That's what Heraclitus - a Greek philosopher - said more than 2,500 years ago.

And when you look at the markets these days, THIS statement seems to fit like a glove.

Do you remember last year, when volatility and volume was very low in the markets? Everybody thought that after the elections in the USA the markets would become more volatile, but they remained dead.

And in 2013 they kept climbing steadily, until the stock market, Gold and Crude Oil saw dramatic drops in April 2013.

How To Trade These Crazy Markets

So how do you trade these markets?

How do you know which strategies are working NOW?

That's where the Rockwell Trading Club can help you.

Every Friday we meet online and conduct a "Mini Trading Course", in which we talk about trading strategies, psychology, tools and other important aspects of trading.

Each of these learning sessions is approximately one hour long . and full of great information that you could use in your trading right away.

Many traders who attended these sessions said that they are like "mini trading courses" - with the difference that you can attend LIVE through the Internet and ask questions right away.

And of course we are archiving these sessions, so you can watch the recording of these sessions any time.

Here's The List

Of The "Mini Trading Courses"

We already conducted 18 Mini Trading Courses, and we are adding new courses to our library every week.

Session 1: Mind, Method, Management - A Roadmap For Trading Success

In this session Mark explains the three pillars of trading and provides you with a roadmap to plan your way to trading success.

Session 2: Avoiding Trading Pitfalls, Proper Planning And Goal Setting

In this session Mark explains how to avoid trading pitfalls and how to proper plan your trading and set realistic goals.

Session 3: How To Develop A Trading Strategy

In this session Mark shows how to develop your own trading strategies. You will see how we develop, test and monitor the strategies we trade.

Session 4: The Simple Strategy

In this session Mark shows how to use indicators to read the direction of the market. You will also learn how to combine these indicators to trade the Simple Strategy.

Session 5: The Ping Pong Strategy - How To Trade Sideways Markets

In this session Mark shows how to identify ideal market conditions for the Ping Pong Strategy. You will learn how to spot entries, and when to consider scalping opportunities as an alternative.

Session 6: The Seahawk Strategy

In this session Rollie reviews his rules for trading the Seahawk Strategy. You will learn how to program auto-exits in Trade Navigator and Infinity AT.

Session 7: The Fibonacci Strategy

In this session Mark shows how to trade Fibonacci Breakouts Retracements. You will also learn how to use this strategy with market reversals.

Session 8: How To Test A Strategy

In this video you will learn the five (5) reasons why you must test a trading strategy and how to backtest without programming a single line of code. This is a "MUST WATCH VIDEO" for every trade!

Session 9: Trading For A Living

In this session Markus discusses how to plan for your trading business. You will also learn ways to analyze your business and plan.

Session 10: Money Management

In this session Mark introduces different money management strategies. You will learn the concept of Fixed Ratio Money Management and techniques for growing a small account.

Session 11: Futures Spread Trading

Very few traders know about this innovative way to trade the markets. Futures Spread Trading can help you to reduce risk while still taking advantage of larger market moves. This strategy is extremely useful for traders with smaller accounts.

Session 12: How to Develop a Trading Plan

In this session Mark discusses the difference between a trading strategy and trading plan. You will learn how to develop a trading plan for your trading business.

Session 13: Forex Trading Strategies

In this session, professional Forex trader Raghee Horner joins Markus and explains how she made $20,000+ per month trading Forex.

Session 14: Trade Efficiency Tools Techniques

In this session Mark discusses ideas and tools for becoming a more efficient trader. You will learn proper journaling and other techniques.

Session 15: How to Identify Major Chart Patterns in the Market

In this session Mark discusses how to identify and use chart patterns to identify trading opportunities. You will learn the major chart patterns and techniques used by traders.

Session 16: Important Tax Information For Futures, Forex, Options Traders

In this session Robert Green of GreenTraderTax discusses important tax information for traders. You will learn how to maximize tax benefits on y our 2012 tax filings and how to get even more breaks for 2013.

Session 17: Monkeys, Wooden Legs, Your Trading

In this session special guest Glen Larson, President of Genesis Financial Technologies Inc. discusses lessons he's learned from programming and working with some of the most famous traders over the last 30 years.

Session 18: Rob Booker's Hopper Trading Strategy

In this video Rob Booker shares his techniques for trading the forex markets and rules for a trading strategy that he uses every day.

Session 19: Technical Analysis Tools And How To Use Them

In this video Markus Heitkoetter discusses popular technical analysis tools like Keltner Channels, Parabolic, ADX, Stochastics, Heikin Ashi Bars and the famous "Turtle Trading" method and how to use these methods to spot market entries and exits.

Session 20: Candlestick Patterns All Traders Should Know

In this video Mark Hodge reviews candlestick formations that all traders should know. He'll also introduce the idea of "Range Candles" and discuss how to trade them.

Session 21: Tips For Using Trade Navigator

In this video Mark Hodge discusses and demonstrates tips and techniques he uses in the Trade Navigator software platform to day trade the futures markets.

Session 22: How To Trade With Stochastics

In this video Markus Heitkoetter introduces the Stochastics indicator and shares a strategy for trading swing reversals in the markets.

(May 17th) Session 23: How To Trade Seasonal Spreads

In this video Markus Heitkoetter will share how to use seasonality in identifying potential spread trades.

(May 24th) Session 24: Ten Things I Have Learned As A Trading Coach

In this video Mark Hodge will share his keen insights after seven years of working with hundreds of traders as personal day trading coach.

(May 31st) Session 25: Introduction To Options Trading

In this video Markus Heitkoetter and Mark Hodge will use their unique teaching styles to explain the fundamentals of options trading using clear, simple, and easy-to-understand presentation techniques.

(June 7th) Session 26: Advanced Options Trading

In this video Markus Heitkoetter and Mark Hodge will share popular techniques for trading options in various time frames and across a number of financial markets.

(June 14th) Session 27: Tips For Using Tradestation and Thinkorswim Trading Platforms

In this video our experts will share with you how to use Tradestation and Thinkorswim to trade the markets using Rockwell indicators and strategy templates.

(June 21st) Session 28: Swing Trading Stocks And ETFs

In this video Mark Hodge will share his favorite techniques for spotting and swing trading stocks and ETFs.

(June 28th) Session 29: How To Trade The Summer Break

In this video Markus Heitkoetter will share why trading during the Summer months can be challenging, and how to prepare to make the most out of potential limited trading opportunities .

The Rockwell Trading Club

If you decide to join the Rockwell Trading Club, here's what you'll get:

Instant Access To All Learning Sessions

Catch up on he latest strategies, tips, tricks, techniques and tools to get ready for these crazy markets.

Live Sessions Every Friday

Join us on Fridays from 12:00pm until 1:00pm when we conduct new learning sessions live and ask us any questions. If you can't make it, we'll record the session and you can watch it later.

Learn From The World's Best Traders

Once a month I am inviting one of my trading friends to share the trading strategies they are using now. We already had Raghee Horner and Rob Booker on the show, and we will have great traders like John Carter, Hubert Senters, Gavin Holmes, Casey Stubbs, Jennifer Thornburg sharing their strategies in the next few months.

Special Bonus

If you decide to join the club today, we will send you 5 DVDs with the Rockwell Day Trading Strategies.

The membership fee if $47 per month . and you can cancel any time.

The bonus DVDs are yours to keep, even if you cancel after the first month.

Just click on the button below to join today, and you will get instant access to the recordings of all sessions PLUS the DVDs with the Rockwell Trading Strategies

Dynamic risk calculator

Dynamic risk calculatorHere is a tool that one of our traders developed (who is a CPA). It is a ZIP file.

Simply download the "ZIP" file to your desktop. Then Extract Them to a file on your PC that you name.

If you do not know how to extract ZIP files - CLICK HERE .

1. Tells you how many lots are available to trade at preset risk assumptions - all you need to do is set your opening margin balance and average stop assumption

b. Other details - entry, target, stop, profit/loss

c. Excel comments can be inserted to document your thoughts as the trade progresses

Click here to download the actual ZIP files

Free Risk Management Software

True risk management in forex trading is very often overlooked by traders at all levels. How much are you really risking per trade? Is your risk-reward ratio making you lose all your profit, and then some?

Use this free risk management software from our friends at Pecunia Systems to easily get an overview of what you are actually risking.

Risk Manager is 100% free software . You can download it below (updated July 8, 2013).

Thoughtleaders,llc leadership training for the real world

Thoughtleaders,llc leadership training for the real worldSimplified Strategic Planning

| Aspects of Leadership Applicable to This Course

Our Simplified Strategic Planning Program is a multi-workshop series that teaches participants an effective strategic planning process while at the same time creating a clear, compelling strategic plan for their organization. Participants will:

Set direction through clear articulation of Vision, Mission, and Strategic Filters

Define their organization’s current and desired future state

Clearly articulate the organization’s core competencies

Define a prioritized set of Strategic Filters to evaluate strategic initiatives

Inventory, evaluate, prioritize, and sequence initiatives to support their strategy

Allocate resources to initiatives based on priority and need

Package their work into a compelling strategic plan

The program consists of a combination of out-of-class work combined with a series of workshops (typically three sessions) and targeted out-of-class coaching. The program is pragmatic in that participants learn the strategic planning method while simultaneously applying it to their real-world business need.

The target audience for this course is the leadership team of a business, business unit, or organization. Putting an existing leadership team through this program enhances both their strategic planning skills as well as the quality of their actual strategic plan. The benefits of this course are:

The leadership team having a strategic planning process they can replicate each year thereby making their plans more compelling and rigorous with less effort than ad hoc planning efforts

An efficient means of gathering and processing key inputs for strategic planning

Better strategic alignment, prioritization, and resource allocation

Clarity and focus which eliminates low value initiatives and drives critical ones

Please contact us to take your team to go through our Strategic Planning Program.

Hear Mike Sheehan and Mike Figliuolo discuss our strategic planning process:

What do our clients think?

Ive been through a couple of strategic planning processes in my career that resulted with the usual mission, vision, and objectives that quickly ended up in a file drawer or on a shelf. However, thought LEADERS provided a straightforward process, a facilitator that was flexible but focused on the end goals, and the help we needed to identify our strategic goals. More importantly, we identified the specific initiatives (including priority and timing) that will help us attain our goals. We now have a framework for evaluating future initiatives and furthering our plan beyond its current horizons. I highly recommend thought LEADERS strategic planning program and facilitation!

thought LEADERS managed a project focused on mapping out strategic alternatives and making key introductions. They worked efficiently and intelligently and the quality of work exceeded our high expectations.

– Founder/Co-CEO, Freedom Financial Network, LLC

Is online trading academy worth it-trusted-safe binary option brokers

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Hlaiman ea generator

Hlaiman ea generatorHlaiman EA Generator

About the Download Installer

Quick Specs

Publisher's Description

From Hlaiman Group: EA Generator is a useful and handy tool for independently developing automated Forex expert advisors allowing you to trade using your own strategy. You don't have to write a single coding line to create an expert advisor. All you need is to place sell and/or buy trades on a chart of a chosen instrument and timeframe in the form of standard graphics objects - arrows. Arrow up is buying, arrow down is selling.

EA Generator software creates complex algorithms which help bring your strategy to life within an automated expert advisor! Thanks to this software, you don't need any programming or math skills or have to pay a programmer to develop an automated expert advisor. You only need to place trades on a chart, get expert advisor and with just a few clicks, this expert advisor will start using the trading principles that were developed exclusively by you.

What's more, you can use EA Generator software to search for new, profitable strategies and use them to create automated expert advisors. The EA Generator software delivery set includes an expert advisor, which will help to automatically place signals about the most profitable trades on any chart. Using this data to create an automated expert advisor can bring profit!

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