Cross-Sectional Mean Reversion Strategy

teddykoker

Here is my cross-section mean reversion strategy from my most recent blog post. It uses an algorithm outlined in Ernie Chan's "Algorithmic Trading: Winning Strategies and Their Rationale." For more information see my blog post. Let me know if you have any suggestions:


class CrossSectionalMR(bt.Strategy):
    def prenext(self):
        self.next()

    def next(self):
        # only look at data that existed yesterday
        available = list(filter(lambda d: len(d), self.datas))

        rets = np.zeros(len(available))
        for i, d in enumerate(available):
            # calculate individual daily returns
            rets[i] = (d.close[0]- d.close[-1]) / d.close[-1]

        # calculate weights using formula
        market_ret = np.mean(rets)
        weights = -(rets - market_ret)
        weights = weights / np.sum(np.abs(weights))

        for i, d in enumerate(available):
            self.order_target_percent(d, target=weights[i])

results

backtrader

Thanks for the contribution and nice post.