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Magic Formula

Magic formula has been discussed in this forum in the past, but not much backtest results have been shared so far. Here I've implemented Greenblatt's strategy, with minor modifications such as filtering out mining and pharmaceutical companies. I've run backtests in segments with different market cap ranges, which showed that eliminating small cap stocks under a billion dollar cap improves the overall return. Small cap baskets tend to get destroyed by a number of companies losing more than 30 percent of their values. As per Greenblatt's remark, the strategy has periods of underperformance compared to S&P, but in long run it does seem to come out slightly ahead.

I'm now interested in comparing the predictive power of fundamental ratios. The original formula weighs return on investment (ROI) and earnings yield (EY) equally, but I found some discussions arguing EY should be weighed more heavily. Ideally I would like to do some regression analysis on these two ratios and other fundamental metrics, but It's difficult to find free historical fundamental data to test this thesis, so I'm wondering if someone here with some experience can chime in.

Clone Algorithm
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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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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: 59ff8319e79d284013b6580b
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