help on paststat backtest report
Example of detailed backtest for SLV (iShares Silver Trust) when the price is up for 3 days in row ->
User can change the lookback period by selecting the “lookback period” drop down menu (can select, over last 1, 2, 3, 4 years)
User can change the exit period by selecting the “exit after “drop down menu (can select, @Next Open, 1, 2, 3,4,5,10,20 days)
User is presented on the right hand side with all the previous instances (dates) when the trading signal triggered in the historical backtesting period. The chg , chg% are the change and change% details for the particular symbol and the next 1,2,3,4,5,10,20 ( depending on the user selected values , default is 1 day) days , for that particular trading strategy.
The detailed backtest report will have the following fields and the description below
Trading Strategy: This is the Trading Strategy the user is performing the backtest
LookBack Period: can be set to either 1,2,3,4 years, the default value is 4 years if the user doesn’t select any value
Exit After: can be set to either @ Next Open, 1, 2, 3,4,5,10,20 days, the default value is 1 day if the user doesn’t select any value.
If the user selects 2 years as look back period and 5 days as the exit after, values, the back test report will be performed over the last 2 years on the stock for the selected predefined screener , assuming the exit period is after 5 trading days at close.
@Next Open Option enables the user to test a trading strategy under the assumption that the trade is entered at the next day open and exit at next day close.
Total # Trades: Number of trades generated by the trading strategy on a stock over the last four years, or if the user selects any other look back period like 1,2,3,4 years
Preference: Long or short, based on the Historical Backtesting Report. Arrived at summing all the change% values and if negative usually going short was profitable, if positive going long was profitable.
Percent Profitable: Number of winning trades expressed as percentage.
# Win Trades: Number of winning trades generated by the trading strategy.
# Loss Trades: Number of losing trades generated by the trading strategy.
Avg Trade (%): The average profit per trade (in percentage) for all the trades has been in the last four years, minus commission and slippage.
Average Win Trade (%) : The average profit per winning trades for all the winning trades has been in the last four years, minus commission and slippage. The average Win Trade is sum of the percentage profits for all the winning trades divided by number of winning trades.
Average Loss Trade (%) : The average profit per losing trades for all the losing trades has been in the last four years, minus commission and slippage. The average loss trade is sum of the percentage profits for all the losing trades divided by number of losing trades.
Median % : in probability theory and statistics, median is described as the numerical value separating the higher half of a sample, a population, or a probability distribution, from the lower half. The median of a finite list of numbers can be found by arranging all the observations from lowest value to highest value and picking the middle one. If there is an even number of observations, then there is no single middle value; the median is then usually defined to be the mean of the two middle values.
The median can be used as a measure of location when a distribution is skewed, so, it’s important to view the median profit per trade (and profit percentage per trade as well) to be in trading strategies favour. For example if the average profit per trade is, let’s say 0.5% and median profit per trade is -0.2%, avoid the system.
Largest Win Trade (%): This is more important than the largest single losing trade. Why? Suppose, for example, your total hypothetical profit was 40%, and say 20% of this is attributed to only one trade, and then what you have is a distorted average trade figure. It’s often a good idea to remove such an exceptional single trade from the overall results and re-compute the system performance in order to confirm whether the trading system is actually good enough to trade. In real life trading, be as realistic as possible and be prepared that you may never encounter that largest winning trade derived from the back tested results.
Largest Loss Trade (%): This measure indicates how much of the drawdown the result of a single losing trade is. In real-life trading, this helps you adjust the initial stop loss. For example, if the average losing trade was 1% and the largest single losing trade was 3% as you would readily guess, a good portion of the average losing trade is borne by the largest losing trade. If you had a better way of managing the largest loser, your overall system performance would be considerably better. In real life trading be prepared to encounter an even higher largest loss, than thrown up by back tested results and brace yourself to handle such situation.
Max Consec Wins: The maximum number of consecutive winning trades generated by the trading strategy.
Max Consec Loss: The maximum number of consecutive winning trades generated by the trading strategy.
Ratio Avg Win / Avg Loss %: Also referred as Payoff Ratio, Payoff Ratio is the system’s average profit in percentage terms per winning trade, divided by the average loss in percentage terms per losing trade. Unless the trading system has a particularly high win/loss ratio, look for payoff ratios of more than 2.
Profit Factor: Profit factor is the system’s gross profit in rupee terms divided by gross loss in rupee terms. Look for systems that have a profit factor of 2.5, or higher.
Outlier Adj Prof Factor: With any trading system, you are going to have one or two exceptional wins. The chances of these trades recurring in the future are very slim and shouldn’t be considered in the overall performance summary. It is often a good idea to remove the largest single winning trade while calculating the outlier adjusted profit factor.
T-Test: The t-Test is a simple statistical test that tells you how likely these test results are to have occurred by chance alone. A t-Test of less than 1.6 favors chance, above 1.6 and one is more likely to have found something real – a tradable key idea. The higher the score given (over at least 20 sample size) the more likely one has found a tradable history.
The t-test is calculated as
t -test= square root (n) * (average trade %/ standard deviation of trades %)
A more stringent t-test value to look for is 2.1 for degrees of freedom 25 (or sample size). As the two tailed P value at t-test of 2.1 for a sample size of 25 equals 0.046 which by conventional criteria, is considered to be statistically significant.