How I Develop Trading Strategies | Permutation Tests and Trading Strategy Development with Python

in this video I'll showare the thought process and four steps I always use When developing a new trading strategy the approach is generic and compatible with almost every strategy I've shown bits of this process in my past videos but I wanted to make a standalone dedicated video I can reference back to in the

future the four steps are insample excellence in Sample Monte Carlo permutation test walk forward test and walk forward Monte Carlo permutation test I'll show an example going through all four steps and generalize the concepts so you can try with your own strategies first I'll show how I assess a trading strategy use a moving average

crossover as an example we load in Candlestick data and compute a fast and slow moving average at each bar we check if the fast moving average is above the slow moving average we create a signal that denotes the position of the strategy at each bar one means we have a long position following that bar and

zero means we have no position regardless of what the strategy is one should be able to create a similar signal a value denoting the position of the strategy after each bar now if we compute Clos to close returns and shift them forward by one bar we can multiply the positions signal by the shifted

returns to get a return for each bar that is attributable to the strategy these strategy returns at the same granularity of the bars are what I use to compute objective functions such as the profit factor or the sharp ratio by having a return for each bar instead of each trade objective functions are ped

much more data and the calculations and results are much more stable the book testing and tuning market trading systems provides convincing Arguments for why these higher granularity returns are superior it's where I got the idea and I'm thoroughly convinced both from what the book says and and my own experience I'll scrap the moving average