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Hi QT, thanks for sharing. It is a very stable strategy
With higher beta diff, higher probability to get larger return?
may be you could plot a curve between the position return and the beta diff to find the best beta diff with high return
@QT,
You are computing beta on daily returns and relating it to actual minute returns at execution. The beta ratio on daily returns does not scale down to minute returns. Simply stated, beta to market on daily returns is NOT EQUAL to beta to market on minute returns!
@QT,
Just trying to help but if you say it works for you then fine, knock yourself out. I'd rather you show the community how and why it works. I have seen several backtest examples here in this forum using minute frequency with trades executed in either market or limit order mode, with or without commissions and slippage that shows great Sharpe ratios and other metrics. I comment on these purposely to spare the newbies and students from blind or fake illusions. The Q platform is not geared or designed to execute trades on minute bars / frequencies mainly because of two things: (1) there is a known bug in executing limit orders and (2) there is no realistic slippage model since Q only provides OHLCV data and what is needed is Level 2 (order book) to account for latency, liquidity and spread of slippage components.
QT, I perfectly understand where you're coming from, no problem there. Yes, you're correct, Optimize API does not process limit orders.
@QT,
When you run the tearsheet of your backtest, do you see these warning messages?
warnings.warn(warning_msg)
/usr/local/lib/python2.7/dist-packages/pyfolio/perf_attrib.py:611: UserWarning: This algorithm has relatively high turnover of its positions. As a result, performance attribution might not be fully accurate. Performance attribution is calculated based on end-of-day holdings and does not account for intraday activity. Algorithms that derive a high percentage of returns from buying and selling within the same day may receive inaccurate performance attribution.