Backtest results for Larry Connors "Double 7" Strategy

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đź—Ł Stock Market Today: China hedge funds caught out by abrupt market surge

China's recent stock market rally, spurred by government stimulus measures, has caused significant losses for some of the country's largest hedge funds, including Beijing X Asset Management, Techsharpe Quant, and Shenzhen Chengqi Funds. These funds were caught off guard by a sudden surge in stock prices, forcing them to cover short positions in stock index derivatives, a key component of their market-neutral strategies. The rally, which followed efforts by authorities to stabilize China's economy and hit growth targets, caused futures prices to rise, undermining the funds' hedging strategies. As a result, some funds saw losses exceeding 5% during the volatile period in late September.

This market upheaval also impacted Winton, a British hedge fund, which had to unwind bearish positions as China's stock prices recovered rapidly. The drawdowns exposed the risks of relying too heavily on quantitative strategies in China's unpredictable market. As regulators continue to crack down on short-selling and data-driven quant strategies, market volatility has increased. Hedge fund managers, like Tim Cao, are reconsidering their reliance on quantitative models in favor of qualitative analysis to navigate the wild swings in the market. Recovery for these funds is expected to take time, with some still nursing the wounds from the market's sudden reversal.

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Today, we will dive into “Backtest results for Larry Connors "Double 7" Strategy" 👇

I tested the “Double 7” strategy popularised by Larry Connors in the book “Short Term Trading Strategies That Work”. It’s a pretty simple strategy with very few rules.

Setup steps are:

Entry conditions:

  • Price closes above 200 day moving average

  • Price closes at a 7 day low

If the conditions are met, the strategy enters on the close. However for my backtest, I am entering at the open of the next day.

  • Exit if the price closes at a 7 day high

Backtest

To test this out I ran a backtest in python over 34 years of S&P500 data, from 1990 to 2024. The equity curve is quite smooth and steadily increases over the duration of the backtest.

Negatives

To check for robustness, I tested a range of different look back periods from 2 to 10 and found that the annual return is relatively consistent but the drawdown varies a lot.

I believe this was because it doesn’t have a stop loss and when I tested it with 8 day periods instead of 7 days for entry and exit, it had a similar return but the drawdown was 2.5x as big. So it can get stuck in a losing trade for too long.

Variations

To overcome this, I tested a few different exit strategies to see how they affect the results:

  • Add stop loss to exit trade if close is below 200 MA — This performed poorly compared to the original strategy

  • Exit at the end of the same day — This also performed poorly

  • Close above 5 day MA — This performed well and what’s more, it was consistent across different lookback periods, unlike the original strategy rules.

  • Trailing stop — This was also good and performed similarly to the 5 MA close above.

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Based on the above. I selected the “close above 5 day MA” as my exit strategy and this is the equity chart:

Results

I used the modified strategy with the 5 MA close for the exit, while keeping the entry rules standard and this is the result compared to buy and hold. The annualised return wasn’t as good as buy and hold, but the time in the market was only ~18% so it’s understandable that it can’t generate as much. The drawdown was also pretty good.

It also has a decent winrate (74%) and relatively good R:R of 0.66.

Conclusion:

It’s an interesting strategy, which should be quite easy to trade/automate and even though the book was published many years ago, it seems to continue producing good results. It doesn’t take a lot of trades though and as a result the annualised return isn’t great and doesn’t even beat buy and hold. But used in a basket of strategies, it may have potential. I didn’t test on lower time frames, but that could be another way of generating more trading opportunities.

Caveats:

There are some things I didn’t consider with my backtest:

  1. The test was done on the S&P 500 index, which can’t be traded directly. There are many ways to trade it (ETF, Futures, CFD, etc.) each with their own pros/cons, therefore I did the test on the underlying index.

  2. Trading fees — these will vary depending on how the trader chooses to trade the S&P500 index (as mentioned in point 1). So i didn’t model these and it’s up to each trader to account for their own expected fees.

  3. Tax implications — These vary from country to country. Not considered in the backtest.

Code

The code for this backtest can be found on this github: https://github.com/russs123/double7

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