Trading strategy python code

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• Introduction.
• hull moving average trading system?

Traders usually have an easier time sticking to the strategy by holding emotions in check. This helps them understand the implications of their strategy on real-time data and help them determine the probability of winning or losing trade. Preserving Discipline in a volatile market: When the market is volatile, traders jump the trading rules.

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This brings about indiscipline in the market. Discipline is lost when a trader gives in to the human emotion of greed or fear. Such parameters can be avoided with Automated trading. Automated trading helps ensure the maintenance of consistency, ensuring that execution of strategy follows rules. Recommended Articles. Article Contributed By :. Easy Normal Medium Hard Expert. Most popular in GBlog. Most visited in Python. Adding new column to existing DataFrame in Pandas Python map function Read a file line by line in Python Python program to convert a list to string How to get column names in Pandas dataframe.

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In detail, we have discussed about. If you just found this article, see Part 1 and Part 2. As a reminder, the dataframe containing the three "cleaned" price timeseries has the following format:. One of the oldest and simplest trading strategies that exist is the one that uses a moving average of the price or returns timeseries to proxy the recent trend of the price.

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The idea is quite simple, yet powerful; if we use a say day moving average of our price time-series, then a significant portion of the daily price noise will have been "averaged-out". Thus, we can can observe more closely the longer-term behaviour of the asset. Let us plot the last 22 years for these three timeseries for Microsoft stock, to get a feeling about how these behave. It is straightforward to observe that SMA timeseries are much less noisy than the original price timeseries.

Using Pandas, calculating the exponential moving average is easy.

Let us attempt to use the moving averages calculated above to design a trading strategy. Our first attempt is going to be relatively straghtforward and is going to take advantage of the fact that a moving average timeseries whether SMA or EMA lags the actual price behaviour. Bearing this in mind, it is natural to assume that when a change in the long term behaviour of the asset occurs, the actual price timeseries will react faster than the EMA one.

Therefore, we will consider the crossing of the two as potential trading signals.

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Well for this strategy it is pretty straghtforward. All we need is to have a long position, i. How is this implemented in Python? Let us examine what the timeseries and the respective trading position look like for one of our assets, Microsoft.

Now that the position our strategy dictates each day has been calculated, the performance of this strategy can be easily estimated. These are calculated as:. Note that our strategy trades each asset separately and is agnostic of what the behaviour of the other assets is. Whether we are going to be long or short and how much in MSFT is in no way affected by the other two assets. To get all the strategy log-returns for all days, one needs simply to multiply the strategy positions with the asset log-returns.

Remembering that the log-returns can be added to show performance across time, let us plot the cumulative log-returns and the cumulative total relative returns of our strategy for each of the assets. Strictly speaking, we can only add relative returns to calculate the strategy returns. Thus, an alternative way is to simply add all the strategy log-returns first and then convert these to relative returns. Let us examine how good this approximation is. As we can see, for relatively small time-intervals and as long the assumption that relative returns are small enough, the calculation of the total strategy returns using the log-return approximation can be satisfactory.

However, when the small scale assumption breaks down, then the approximation is poor. Therefore what we need to remember the following:. One can observe that this strategy significantly underperforms the buy and hold strategy that was presented in the previous article. Let's compare them again:.