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Backtesting Strategies with R
This means that every time you visit this website you will need to enable or disable cookies again. Home Hacks Tips Contact About. George Pipis September 4, 5 min read. Few words about Trading Strategies One of the biggest challenges is to predict the Market.
Define the Trading Strategy For illustrative purposes, we defined an arbitrary Trading Strategy which does not make much sense, but it is good to work on it as an example. R Code for to backtest the Trading Strategy You can have a look at how we can get the Cryptocurrency prices in R and how to count the consecutive events in R. Below we build a function which takes as parameters: symbol : The cryptocurrency symbol.
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For example, BTC is for the Bitcoin. SL : The percentage that we stop loss.
TP : The percentage that we take profit. Share This Post. Share on facebook.
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Share on linkedin. Share on twitter. Share on email. Subscribe To Our Newsletter. Get updates and learn from the best. Prev Previous How to use global variables inside a function. Next step is to pick a trading strategy. In a moving average crossovers strategy two averages are computed, a slow moving average and a fast moving average.
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The difference between the fast moving average and the slow moving average is called MACD line. A third average called signal line; a 9 day exponential moving average of MACD signal, is also computed. If the MACD line crosses above the signal line then it is a bullish sign and we go long. If the MACD line crosses below the signal line then it is a bearish sign and we go short. We choose the closing price of NSE data to calculate the averages. Following command fulfils this task.
One can choose varying parameters for fast, slow and signal averages depending upon the trading requirements. Here we stick to the standard parameters. Setting it TRUE would return the percentage difference between the fast moving average and slow moving average.
Following command generates the trading signal accordingly. We use the lag operator to eliminate look ahead bias. We will apply this strategy on the historical data of NSE from to The trading signal is applied to the closing price to obtain the returns of our strategy. The ROC function provides the percentage difference between the two closing prices. We can choose the duration for which we want to see the returns.
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The following command chooses the returns between and The 4 th step of back-testing is evaluating performance metrics. The performance analytics package in R provides a consolidated platform to observe performance related parameters. Various metrics like draw-downs, downside risk can be observed in R. In this post we illustrated a very simple strategy and saw how you can break down a premise into the reaction to events.
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