Evaluating Backtest Results/ Constructive Criticism
There was a trading strategy which I used to manually execute a few months back but am no longer able to as things just got busier. So, I thought I could somehow automate it but it took a while to figure out the data sources and to convert the strategy from what was in my head to actual code. I recently finished implementing it and did a custom backtest using data starting from 2018. I'm still fairly new to algo/quant trading although I have been manually trading for some years - so I would like to get some help on how I can evaluate my backtest results.
Here are the details of the backtest & strategy:
Strategy is just rolling statistics based, no ML or anything fancy.
Backtest time period: Jan 2018 - present (can't go further back than jan 2018, limited by data)
Stock universe: only trading US stocks with daily average turnover >$5M since inception/IPO
Timeframe: Interday
Strategy overview: Long only, news-reaction based combined with volume & price action (all data is adjusted). Signal (just says long or nothing) is generated EOD and trade is executed at open next morning at market price. No take-profit or stop-loss used in backtest. Position is sold at close 5 days after initial buy - this is fixed as I didnt want to make it too complex to begin with.
Sharpe: 4.02
T-test: Tested if the mean daily log returns are >0. I'm getting a p-value of 0.0067 but I'm a bit skeptical of using the t-test here as I'm not sure if the log-returns of the strategy actually meet the assumptions for this test.
Total #trades since inception: 231
%Profitable trades: 55.8%
Mean daily portfolio holdings P/L(%): 1.06% (includes days where gains were unrealized)
Cumulative returns over backtest: ~140%, the profits from one trade(buy+sell) are not reinvested.
Max leverage at any time: 3.6
Max #stocks held at any time: ~12
Position Sizing: Fixed, same amount of money invested in each stock.
In the graph below the red line/markers show the # of stocks in the portfolio at that time.As you can see the strategy trades way less frequently in 2019... I'm not sure why this is ? I'm coming to think that this strategy trades less in bear/uncertain markets as compared to bull.
Posted Jan 10, 2020, 8:28 PM UTC
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I’m not sure how many more details I can give about the alpha without actually giving away the strategy - let me try to think about a way to explain the strategy in a more generic way so that you can comment.
Regarding the slippage - that’s an interesting point and could probably add some points to the return per trade if implemented in real time. But for backtesting I’m not sure if it would make a significant difference ? Correct me if I’m wrong.
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