An Entry Strategy is the set of rules specifying the conditions to enter a trade. 

For a long trade, entering means buying a stock.   For a short trade, entering means selling the stock.

Extra Insight:

Having a strategy for entry allows a trader to plan with a cool head rather than getting caught up in the heat of the moment.   Backtesting the entry strategy gives a trader insight and confidence in the plan.

The main goal of an entry strategy is getting into profitable trades.  on the flip side, it is useful to stay out of losing trades, making it a Do-Not-Enter Strategy as well.

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Updated 11/12/08.

Tags: backtesting, entry, goals, long, short, strategy, trading

 The Exit Strategy is a well-defined plan specifying the conditions to get out of a trade.  

 For a long trade, exiting means selling the stock. For a short trade, exiting means buying a stock.

Extra Insight:

Having a strategy for exit allows a trader to plan with a cool head rather than getting caught up in the heat of the moment.   Backtesting the exit strategy gives a trader insight and confidence in the plan.

Most traders have two purposes for exiting:  taking profits and cutting losses. 

Sometimes both ends are served by one exit order, such as a trailing stop.    Other times, they are two distinct orders, such as a fixed stop loss and a target limit order.

A third goal of an exit strategy may be the efficient use of capital.    In that case, the exit strategy may have rules to exit a trade that isn’t going anywhere in order to redeploy the resources elsewhere.

Click here for BackTesting Reports on Exit Strategies

(Backtesting Blog is an Amazon Associate.)

Updated 11/12/08.

Tags: backtesting, exit, goals, loss, stop, strategy, trading

Expectancy measures a trading strategy’s profit potential.   It considers both the reliability or win rate as well as the amount gained by each win.    That way, it can compare trading strategies that often win small gains with strategies that rarely win but win big when they do. 

Expectancy = (win_rate * avg_win) – (loss_rate * avg_loss)

Van Tharp defines expectancy in terms of risk here, as the average of the R-multiples returned by trading or backtesting the system.

Extra Insight:

Over a large number of trades, the expectancy is the expected gain of the trading strategy.  Higher expectancy is generally better.  Always avoid trading strategies with negative expectancy.

Scaling the expectancy by risk is indeed useful, especially when it comes time to compare different systems.  I use the R-multiples as suggested by Van Tharp for ease of calculation.

Expectancy is also known as the Kelly Criterion for the Bell Labs researcher who proved the equation as an upper bound on the amount to risk.     A common language way to say it is to risk an amount proportional to the expected gain.   So if the expectancy is 45%, Kelly advocated risking 45% of the account value.   This may be mathematically optimal over a large number of trades but it can have a very vicious drawdown!   Imagine trading a high expectancy system, say 80% and the first trade is a loss.  For a $100k account, that would leave only $20k in the account and a long road to make a 4x gain to break even.

Expectancy is not the be-all and end-all of a trading system.   The standard deviation or variance of the results is important.  The win rate is too.  Both give insight into how psychologically difficult it is to stick to the trading strategy.

Updated: 11/12/08.

Tags: backtesting, expectancy, Kelly criterion, long, R-Multiple, risk, strategy, system, trading, Van Tharp

Forward-testing, means trading a strategy live with very small size to see how well the strategy (and the trader!) perform in real life.   

Forward-testing is typically done after backtesting to make sure the trading strategy is not the over-optimized result of curve-fitting or data mining.  It also gives a chance to try out the mechanics of entering, tracking and exiting trades.

Extra Insight:

I’ve heard varying advice on the size of trades for forward-testing ranging from smallest possible size - think 1 share – up to just enough to engage the trader’s emotions.

For more insight into this topic, Design, Testing, and Optimization of Trading Systemscomes highly recommended.

(Backtesting Blog is an Amazon Associate.)

Updated: 11/12/08.

Tags: backtesting, curve-fitting, data, exit, forward-testing, sizing, strategy, trading

Backtesting runs on Historical Price Data.  It is the record of actual transactions for a stock, index, commodity, or other market. 

The atomic unit of data is the bar.  Each bar represents one unit of time and has an open price, high price, low price, close price and volume.    For end-of-day data, each bar represents one day.

Extra Insight:

Data comes with the backtesting engine and is also sold separately by data vendors who make it their core business.  Data originates with the stock exchanges who keep records of each transaction.

A weekly chart can be constructed from daily data.   However, its impossible to go to smaller granularity than the available data, e.g. daily data cannot be used to create a 15-minute chart.

Updated: 11/12/08.

Tags: backtesting, data

Price and Other Indicators

An Indicator is an abstraction of historical price data which is used to gain insight into the stock or market behavior.   Examples of popular technical indicators are Moving Averages, Bollinger Bands, MACD lines and histogram, RSI, Stochastic Oscillator.

Stockcharts.com has a good comprehensive definition of technical indicators.

Extra Insight:

Indicators describe past market action in a way that can be calculated by computer.   We can objectively define trading signals in terms of the indicators.  For example, we could define a buy signal as the stock price increasing above a moving average.

Its also possible to use indicators subjectively, but that greatly diminishes their value (IMHO).

Backtesting checks market action subsequent to an indicator’s signal.  Backtesting a large number of stocks over a large time period gives insight into how the indicator performed in the past.   Backtesting can only be done with objective rules for evaluating an indicator.

Even an indicator that tested well in the past may not perform well in the future — there are no guarantees.

An objectively defined indicator can help a trader make crisp decisions and trade by a system of rules rather than be ruled by emotion.

Updated: 11/12/08.

Tags: backtesting, data, indicator, macdh, rsi, stochastic, system, technical, trading

By Instrumentation I mean adding my own special instructions into the code for the trading strategies to produce useful information for analysis. 

The commericially available backtesting engines provide limited data and what they do provide is not in a convenient format to process for thousands of runs.

Tags: backtesting, data, instrumentation, trading

Here’s the scoop straight from the SEC on limit orders. 

Extra Insight:

Limit orders are one way to reduce slippage because you specify the exact price you are willing to accept.

Backtesting with end-of-day data models limit orders reasonably well for liquid markets.    The backtesting engine will execute the whole order if the stock traded at limit the price during the day.  This is what normally happens in live trading.    Sometimes in live trading though, the full limit order doesn’t execute, but only a smaller number of shares are traded at the limit price.   This can happen if the limit order is larger than the market can bear at the time.

A small private trader is unlikely to get a partial fill in a large liquid market.   By sticking to high volume stocks, its possible to understand more about the differences between market orders and limit orders via backtesting.

There are Limit-on-Close (LOC) and Limit-on-Open (LOO) orders which corresponds to Market-on-Close and Market-on-Open orders, respectively.   I don’t use them in backtesting though.

Updated: 11/12/08.

Tags: backtesting, data, limit order, market-on-close, market-on-open, trading

Long means to buy or own a stock.  It is the opposite of short.

Extra Insight:   Hints on usage:

• If you are bullish, you might “buy long” or “get long” or “go long”.
• Or, if you are bearish, you “might “sell or ”sell short” or ”get short” or ”go short” or “short”.
• If you say, ” I am shorting”, that means you are trading in a very bearish way.
• If you say, “I am longing”, that means you are wanting, not trading.

(Backtesting Blog is an Amazon Associate.)

Updated: 11/12/08.

Tags: backtesting, long

A Market on Close (MOC) order is entered before the market closes and the transaction takes place at the day’s closing price.  

The US stock exchanges process these orders.  Check with your broker for exact instructions on how to enter them.

Extra Insight:

Due to using historical end-of-day data, a ”this bar at close” order in backtesting behaves similar to a Market-on-Close order because it takes today’s closing price.   A key difference is that the backtest actually “sees” the closing price before placing the order.   I wish I could do that in real life!

I use the MOC or “this bar at close” order in backtesting only for timed exits because the decision to exit in this case doesn’t depend on the closing price, only the number of days in the trade.

For large orders in thinly traded markets, a live market order might move the live market, resulting in a different closing price than would have occurred without the order – an effect that’s not modeled with historical end-of-day data.

Read a professional’s report here that live MOC orders often execute at the published closing prices.

A small private trader is unlikely to move a large liquid market.   By sticking to high volume stocks, its not only possible to backtest market orders, its also possible to understand more about the differences between market orders and limit orders via backtesting.

Updated: 11/12/08.

Tags: backtesting, data, exit, market order, market-on-close, trading