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Range percentage for purchasing

I have found from experience, that there are just certain historical periods, that no Algo can deal with, this is why they are still manually guided and optimised on the fly.

It would seem that the only real variables that need to be change here are:

[] Purchase % Buy = +3.7% Sell = .-.75% [] Look back should be 7-14 days [] Range should be around 325 days

There is also the additional main governing factor, and that is the price bandwidth (range) as not all price action i.e. Bollinger bands, ATR, CCI, and the its many forms are the same for each position and I have found the optimization of this range paramount, as the scale of volatility does cycle and vary.

Hope this helps, Michael

Clone Algorithm
Backtest from to with initial capital
Total Returns
Max Drawdown
Benchmark Returns
Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 55ae8a89b693190c765ae85b
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1 response

Cool, glad you found my example useful! This can be modified to do a lot of different things, let me know if you have any questions. One other thing I was playing around with was using volume data instead of price data. What you would have to do for that is have the input variables be volume and the output variables be price change, so there would need to be two history calls in create_model and one history call (for volume) in trade.


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