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Reliable backtesting in Tradestation / MultiCharts

StrategyQuant > Reliable backtesting

Reliable backtesting in Tradestation / MultiCharts

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How to reliably backtest strategies in Tradestation / MultiCharts to get the same results in SQ and your trading platform.

Reliable backtesting in MetaTrader

StrategyQuant > Reliable backtesting

Reliable backtesting in MetaTrader

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How to run reliable backtesting in MetaTrader

Simple Optimization

StrategyQuant > Advanced functionality > Optimization

Simple Optimization

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The idea behind an optimization is simple. First you must have a trading system, this may be a simple moving average crossover for example. In almost every system there are some parameters (indicator periods, comparative constants, etc.) that decide how given system behave. The optimization means to test the system with different parameter values to […]

Monte Carlo retest methods

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Monte Carlo retest methods

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This is another type of Monte Carlo simulations, in this case it simulates random changes in properties that require the strategy to be retested – such as changes in spread, slippage, strategy parameters, or history data. Because every simulation requires a complete backtest this cross check could take long time. It the backtest on main […]

Retest on additional markets

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Retest on additional markets

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This test for robustness is quite though – it means testing the same strategy on different markets – it means different bol(s) and/or another timeframe(s). Robust strategy should ideally work on multiple symbols/timeframes. In reality, because each market has its own characteristics, daily volatility, etc., it will be not easy to find a strategy that […]

Monte Carlo trades manipulation

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Monte Carlo trades manipulation

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This cross check run simulations where in each simulation it manipulates the existing trades – shuffles them, misses some and so on. It is very quick, because it doesn’t require running backtests, it works on already existing trades from main backtest. The idea behind this is to verify how much the strategy equity curve depends […]

Retest with higher precision

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Retest with higher precision

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This test is simple – it backtests the strategy again on the same data, but with higher precision. It is usually best to make the main test on the fastest Selected timeframe precision, because it can very quickly filter out bad strategies – these that produce no trades or whose Net profit is negative. Once […]

Use Cross checks build in Builder and Retester

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Use Cross checks build in Builder and Retester

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StrategyQuant X allows you to use cross checks (robustness tests) during the strategy build, or when retesting the strategies. There is a number of cross checks that can be used, ranging from simple ones to very complex ones, and they are simple to choose – you can just move the slider in the simple settings: […]

Cross checks – robustness tests and analysis

StrategyQuant > Advanced functionality > Cross checks - robustness tests and analysis

Cross checks – robustness tests and analysis

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Cross checks – robustness tests and analysis Overfitting or curve-fitting strategy to the historical data on which it was build is the biggest danger of strategies generated using any machine learning process. During or after developing new strategy you should make sure your strategy is robust – which should increase the probability that it will […]

Genetic options

StrategyQuant > Program screens > Settings

Genetic options

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when you set Build mode to Genetic evolution in What to build -> Build Mode, a new tab Genetic options will appear in the settings. There you can set up various properties of genetic evolution. Genetic options Max # of Generations number of generations for which the population will be evolved. Recommended value from 5 […]

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