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RSI / SMA tracking ROI before selling

Hi all,

I'm pretty new to Quantopian and crossing into the Python world in so doing, so please excuse any ignorance.

I started by attempting a simple RSI/SMA algo, but backtests have not been going well.
To curb drawdown, I've tried to validate ROI on mid-level RSI/SMA, while maintaining a strict sell policy on low-level RSI/SMA.

Anyone care to share some pointers?

Clone Algorithm
3
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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: 59f357a839d3ab442b128dd1
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3 responses

Anyone?

I think my feedback can separate into tactical and strategic.

Tactically, take out all that ordering logic and put in the Optimize API. Optimize looks at your current portfolio, accepts a description of your target portfolio, and then issues orders to get you from one to the other. It's much nicer than all of the shenanigans you do with position checking and ordering. It can even manage turnover constraints to reduce transaction costs (which you set to zero anyway).

Strategically, I think you should move on from this idea and try to research another one. Go through the lectures, particularly the long-short equity examples, and the factor analysis ones, and see if you can come up with other ways to tackle the market. What your backtest demonstrates is that this particular method doesn't make money. The unfortunate truth is that most investment ideas don't make money. To succeed, you need to develop the ability to test ideas rapidly; you want to spend your time finding good ideas, not dwelling on bad ones.

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Thanks Dan.

I appreciate the feedback and I'll dig into those lectures.