Building Automated Trading Strategies October 2018 | Page 43

Backtesting may help traders to select the most efficient strategy for
trading a particular asset class
✓ Allows Strategy optimization
Optimization helps to increase the performance of the selected strategy
by modifying secondary parameters or simply some values associated
with that strategy’s implementation process
✓ Final verification
The final backtesting of a strategy ensures the quality and efficiency of
all our re-calculations and adjustments during the optimization process.
Key Performance Statistics
These are the key performance statistics when backtesting automated trading
strategies:
➢ Net profit or loss (in pips) / Average gain or average loss (in pips)
➢ Win-to-loss Ratio
➢ Losing and Winning streak
➢ Max Drawdown % (the percentage of the maximum peak-to-trough
decline during a specific period)
➢ Maximum Exposure % (maximum percentage of capital allocation in
the market)
➢ Annualized return % (return over a year)
➢ Sharpe Ratio (comparing the trading strategy’s returns with the
standard deviation of those returns)
Monte Carlo Analysis 8
Monte Carlo methods can analyze investment portfolios and algorithmic
trading strategies. This is happening by simulating the various sources of
8
https://en.wikipedia.org/wiki/Monte_Carlo_methods_in_finance
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