Building Automated Trading Strategies October 2018 | Page 45

➢ R
➢ MetaTrader Backtesting
➢ Strategy Quant
General Rules for Successful Backtesting
These are some basic rules for successful backtesting:
✓ Perform various backtesting experiments (test your strategy during all
different types of market –bullish, bearish, and ranging)
✓ Backtest your strategy over a long time frame that includes normal and
abnormal market conditions
✓ Volatility statistics are very important for leveraged accounts (for
example if your strategy performed a 60% winning ratio but it had 20
losing streaks in a row, most probably your account will be out of
money in real market conditions)
✓ Use backtesting as a component of a general trading experiment (a
trading experiment that also includes optimization and customization)
✓ Avoid over-optimization. (Over-optimization means that after many
calculations and re-calculations, the results of backtesting are
optimized for past market conditions)
✓ Customization is very important (traders must tune all backtesting
parameters with accuracy and mimic real market conditions)
✓ Successful backtesting cannot guarantee future results. The market
conditions are fully dynamic and strategies that performed well in the
past may fail tomorrow.
Backtesting is an essential process for building successful automated trading
strategies. The key point to remember is that backtesting is not an
autonomous project. Backtesting should be a dynamic component of a
general
trading
experiment,
which
also
includes
customization
and
optimization.
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