Almost 85% of CTA returns can be explained by simple trend following. Momentum or trend following are without a doubt the most popular systematic rule-based strategies used by hedge fund managers.
While moving average models are the ‘hello world’ of trend following strategies, in the era of machine learning, I try to approach it purely from a quantitative perspective where all signals were based on raw price data and its statistical properties.
The strategy is to buy and sell the Indian equity benchmark Nifty Index when the proprietary statistical measure (SM) is above/below a predefined threshold.
Datasets
Nifty futures 1-Min data from August 2010 to August 2019 was used. This data was then further resampled and manipulated to generate trade signals.
Backtesting & Assumptions
To test this concept, I performed quick backtesting using Python from August 2008 to August 2019 with the following assumptions:
- Backtesting assumes zero friction and trades were fully collateralized (meaning returns were calculated on the notional value of the contract and not on margin deployed for the trade).
- The signals were not optimized for this exercise and no short selling was allowed.
- Trade was entered at the close price of the period.
- The position was held until a sell signal is triggered.
- Trade was left untouched and all statistics were recorded between entry and exit levels.
Equity Curve
Backtesting Results
The strategy has generated a CAGR of 11.35% with a maximum drawdown of 6.87% on a fully collateralized basis.
Monthly Returns Distribution
96.94% of the time, the 12-month returns were positive. Nevertheless, the strategy can further be optimized for higher alpha. The analysis shows statistics can be used satisfactorily to generate alpha.
The article was originally published in the LinkedIn. Read more at: https://www.linkedin.com/pulse/can-simple-trend-strategies-work-long-term-kannan-singaravelu-cqf
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