Introduction In the last section we ran a single backtest. However, for our strategy to work we should optimise our strategy parameters. If we blindly run through a large set of parameters and then pick the best one we are very likely to fall for an issue called Data Mining Bias. This means that if…
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In the previous posts we have downloaded market data, developed a vectorised backtest and calculated PnL, Sharpe ratio and drawdown. In this post we will set up, run and analyse a single backtest. This is the basis for running parameter sweeps and optimisations with hundreds or thousands of backtests. So, let’s set some important parameters…
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Sharpe Ratio In the previous sections of Quant Basics we looked at producing data sources and how to write a vectorised backtest. We also calculated our first metric – PnL and tested its functionality. In this section we will add two more metrics that are very important for strategy evaluation: Sharpe ratio and drawdown. Let’s…
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Why Vectorise? There are several ways to backtest a strategy on historical data. In this section we demonstrate vectorisation. This and the previous section will serve as preliminary exercises before we dive deeply into the quantitative section. However, one should not underestimate the pitfalls of backtesting. It is very easy to make mistakes here, so…
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Introduction Welcome to the Quant Basics series. This mini series came from the observation that most people starting in quantitative trading focus almost entirely on the generation of trading signals. While this is important, several other areas in quantitative trading strategy development are even more cruicial such as: Data Vectorised Backtesting Performance analysis and…
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Welcome to the AAA Quants blog. We are a team of passionate bloggers in the space of machine learning and algorithmic trading. Much of what is happening in this space is still new and experimental and we are dedicated to join the movement and share our ideas. We feel that currently there are three technologies…
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