![]() ![]() ![]() Part of the issue lies in the goal of the analysis, i.e. Mixing single period and multi-period attribution is always always a challenge. This is always a troubling situation, as it indicates that some sort of leakage may be occurring in your model. You may have noticed that your individual components do not equal the whole, either in an additive or geometric manner: > cum.tail(1) Mdd, sd, ed = max_draw_down_relative(p, b)į, a = plt.subplots(2, 1, figsize=)Ĭum].plot(title='Cumulative Absolute', ax=a)Ī.axvspan(sd, ed, alpha=0.1, color='r')Ĭum].plot(title='Cumulative Active', ax=a) The drawdown caclulation can now be made analogously using the formula above: dd = (p * b0 - b * p0) / (p0 * b0) p0 = pd.Series(p.ix.values, index=p.index)ī0 = pd.Series(b.ix.values, index=b.index) So, we generate a series of ' whens' captured in cam ( cumulative arg max) and subsequent series of portfolio and benchmark values at those ' whens'. The difference is that we want to keep track of what the p and b were at this time and not the difference itself. We get this series of cumulative active returns with p - b. Similar to the absolute case, at each point in time, we want to know what the maximum cumulative active return has been up to that point. P0 = pd.Series(p.iloc.values, index=p.index)ī0 = pd.Series(b.iloc.values, index=b.index) This is how we can extend the absolute solution: def max_draw_down_relative(p, b):Ĭam = pmb.expanding(min_periods=1).apply(lambda x: x.argmax()) The active return from period j to period i is: ![]() the variables below are assumed to already be in cumulative return space. ![]() Starting with a series of portfolio returns and benchmark returns, we build cumulative returns for both. I wanted to follow up by asking how others are calculating maximumĪssumes that the solution will extend on the solution above. The max drawdown is then just the minimum of all the calculated drawdowns. r = returns.add(1).cumprod()Īt each point in time, the current drawdown is calcualted by comparing the current level of the return index with the maximum return index for all periods prior. We start by generating a series of cumulative returns to act as a return index. It takes a return series and gives back the max_drawdown along with the indices for which the drawdown occured. This is what I implemented for max drawdown based on Alexander's answer to question linked above: def max_drawdown_absolute(returns): I wanted to follow up by asking how others are calculating maximum active drawdown? This calculates Max Drawdown. The daily loss limit for this given day would still be $5000 initially, but since there’s a carried loss of -$1000 then will be deducted from the daily loss limit, and now a remainder of $4000 is left to lose before the account is flagged as Failed as per the daily loss limit.I recently asked a question about calculating maximum drawdown where Alexander gave a very succinct and efficient way of calculating it with DataFrame methods in pandas. The daily loss limit for this given day would still be $5000, but you’re allowed to lose $1000 more of the floating profits for a total of $6000.Īlternatively, if the account balance is $100,000 and your equity is $99,000 (at the close of the daily candle) then the max daily risk for the following day would be 5% of the starting balance of the day $100,000 = $5000, and the losing positions on your P/L results carried over from the previous trading day will be deducted to your daily loss limit, in this case, $5000 – $1000 = $4000. This is the Account balance at the close of the daily candle on broker time.įor example, if the account balance is $100,000 and your equity is $101,000 (at the close of the daily candle), then your max daily risk for the following day will be 5% of the starting balance of the day $100,000 = $5000, and the profits on your P/L carried over from the previous trading day will be added to your daily loss limit. Daily loss limit is calculated based on 5% of the starting balance of each trading day. ![]()
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