know exactly when to take the trade every single time you see it on the chart. Although most backtesting software includes commission costs in the final calculations, that does not mean you should ignore this statistic. All of our trading strategies have been thoughtful backtested so it can prove to ourselves that we have an edge in the market. What are your experiences with backtesting? As an example, I chose to backtest a strategy on, bitcoin as its trendy in these recent time. Also, please give this strategy a 5 star if you enjoyed it! Hopefully, you were able to see the beauty of combining different Python libraries to manipulate, analyse and visualise data. So, now that we know what kind of strategy were going to be backtesting were going to highlight the key components needed not just to backtest this kind of strategy but they are universal components that can be used as a template for backtesting any.
Now, that we have created our entry techniques we need a stop and take profit strategy. Here is another strategy called Time-Based Trading Strategy. If you find enough and strong evidence that some days tend to produce better results for the double top/double bottom pattern then you should focus more to take the trades during those days with the best potential. This will take some time. DataFrame(indexlags, columns leads) If we run len(lead_lags) we will see that we have to make 1600 backtests to fill up the pnls matrix. All you need for this is a python interpreter, a trading strategy and last but not least : a dataset. A successful 2 year backtest will never certify that your strategy will be successful in the future. At the end, we will plot the heatmap of these PnLs as function of period combinations.
In other words, did you try trading over a particular period, and then backtesting the same strategy over that period, to compare the results of the backtest versus the results of reality? This seems like the best approach in terms of determining what is causing the discrepancy.