Trading strategy parameter optimization

The aim of any optimization is to adjust one's trading system in an attempt to make it more effective. Strategy optimization is searching for optimum parameters​.
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A trading strategy is the process used to enter and exit positions in a market based on quantified signals on when to buy and sell.

A successful strategy should have an edge expressed in how trades are entered and managed to maximize gains and minimize losses. Creating a trading strategy from beginning to end requires several steps to complete the process. A trader has to first pick what type of method they want to use to make money. What markets will the strategy be trading?

How to develop, test and optimize a trading strategy – complete guide

There are many to choose from stocks, ETFs, options, forex, futures, and crypto. Will a trader be a discretionary trader of mechanical trader and if both what mixture of each. All strategies have some level of discretion built in from building, picking a watchlist, and choosing risk parameters. A trading strategy has three primary processes, they are entries, exits, and position sizing.

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An entry signal should get a trader into a trade when the odds of it being a winner are greatest. Whether a momentum signal is entered because the odds are a breakout will continue to go in the path of least resistance or a dip is bought because the odds are that at an extreme oversold level that price will bounce, both setups should have a higher probability of being a winner.

An exit has two primary purposes, one can be to keep a loss small and the other is to lock in profits while they are still there.


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A stop loss keeps losses small when the odds are that the trade is not going to work out and a trailing stop signals it is time to lock in a winner when a trend starts to bend. Use of this web site signifies your agreement to the terms and conditions.

Optimization and analysis -RobinTradingHub

Optimizing parameters of algorithm trading strategies using MapReduce Abstract: In algorithm trading, computer algorithms are used to make the decision on the time, quantity, and direction of operations buy, sell, or hold automatically. To create a useful algorithm, the parameters of the algorithm should be optimized based on historical data. However, Parameter optimization is a time consuming task, due to the large search space.

A simple strategy might be to buy when the relative strength index goes below 35 and sell again once it returns to After defining the features and the trading rules, running the backtest is only a matter of putting the components all together:. Besides backtesting existing trading strategies, BaaS can optimize trading parameters automatically and even find new strategies from the data. To optimize parameters, BaaS only needs to know which interval of parameter values to consider:.

For the span parameter, it considers values between 1 and 20 minutes.

Parameter Optimisation for Systematic Trading

BaaS uses state-of-the-art optimization techniques to search for winning parameters efficiently. This makes parameter optimization possible—even for complex strategies with many parameters. One of the strengths of BaaS is that it scales horizontally in the number of days processed. This means that it takes approximately the same time to run a backtest on one week or on ten years of data after loading the input data : BaaS transparently provisions extra machines.


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BaaS also scales horizontally in the number of stocks that a strategy is tested on. With BaaS, this is no problem: the running time would not increase.

What is BaaS

The running time only increases in the number of stocks that a strategy trades simultaneously. So, if you have a single strategy that trades ten stocks, this will in general be slower than testing a strategy that trades only one stock. For most use cases, this is not a problem at all, since strategies typically only use a handful of stocks.

Trading Strategy Optimization (User Guide)