diff --git a/val_sim.py b/val_sim.py index 8fb2cb858fcc450a94cc36cd8d87b746443d0f9e..313e24489ce1cdc316dd8c52b43fa476e20663f1 100644 --- a/val_sim.py +++ b/val_sim.py @@ -22,14 +22,12 @@ dates = [] for i in range(minhistory, len(close)): if t == "buy": - if buy[i] == 1: # if we’re interested in Buy signals + if buy[i] == 1: close_data = close[i-minhistory:i] pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))] - mn = mean(pct_change) - std = stdev(pct_change) - # generate much larger random number series with same broad characteristics - simulated = [random.gauss(mn,std) for x in range(shots)] - # sort and pick 95% and 99% - not distinguishing long/short risks here + mean_value = mean(pct_change) + std_value = stdev(pct_change) + simulated = [random.gauss(mean_value,std_value) for x in range(shots)] simulated.sort(reverse=True) var95 = simulated[int(len(simulated)*0.95)] var99 = simulated[int(len(simulated)*0.99)] @@ -37,14 +35,12 @@ for i in range(minhistory, len(close)): var99_list.append(var99) dates.append(str(dt[i])) elif t == "sell": - if sell[i] == 1: # if we’re interested in Sell signals + if sell[i] == 1: close_data = close[i-minhistory:i] pct_change = [(close_data[i] - close_data[i-1]) / close_data[i-1] for i in range(1,len(close_data))] - mn = mean(pct_change) - std = stdev(pct_change) - # generate much larger random number series with same broad characteristics - simulated = [random.gauss(mn,std) for x in range(shots)] - # sort and pick 95% and 99% - not distinguishing long/short risks here + mean_value = mean(pct_change) + std_value = stdev(pct_change) + simulated = [random.gauss(mean_value,std_value) for x in range(shots)] simulated.sort(reverse=True) var95 = simulated[int(len(simulated)*0.95)] var99 = simulated[int(len(simulated)*0.99)]