@James, in your
section you have: largest winning trade: $216,922 while total profit came in at $2,270,658. This outlier alone is 9.55% of total profits. I suspect that the next 100 in line would obliterate total profit. That would be some 100 trades out of 203,200 accounting for all the profits. It is less than 0.049% of all trades taken. In your backtest, only the first 86 highest trades were needed to exceed that mark. So, in this case, might as well forget the 20/80% rule of thumb. Moreover, UFS and MSI accounted for over 20% of the P&L (2 stocks out of the thousand+ traded).
I do not see how Q or anybody else on the planet could leverage, as is, that particular trading strategy to something like 5-8 times, even with free capital (OPM). No matter who they are or how creative they could be in financial engineering. Managing money will cost something even if you could get below LIBOR rates and not consider such things as needed personnel, hardware and software monitoring and expenses, opportunity costs, alternative investments, margin, leveraging and management fees. Here is another simple question: how much 20+ years of your time on this kind of problem is worth to you? Because that is also part of the price to pay.
The decay of your trading strategy is built-in. The strategy insists on a 50/50 long/short scenario for market-neutrality. In your trade execution summary, the data should show the equivalent of what should be viewed as an average number of transactions per period. Make it an average number of trades per month or per year. Your average daily turnover chart, for instance, says just that.
Your average net profit per trade ended with \( \bar x = \$11.17\). As time progresses, \( \bar x \) will continue to decrease. Most of the trades are concentrated near zero. See your “P&L per round trip trade in $” chart for a confirmation. The strategy is expected to continue to generate some 29,000 trades per year. To see this more clearly, redo the simulation with start date: 2008-07-01 and with end date 2018-08-22.
Note that it is not just your trading strategy that is behaving this way. I think every strategy participating in the contest suffers from the same thing. Nowhere on Quantopian have I seen, in all the examples provided or the discussions anyone even trying to compensate CAGR degradation by any means. I would have to conclude that none are doing it. How could they compensate when they don't even look at the total picture, but just period to period? They could so easily prove me wrong by showing that they do it by showing their tearsheets. I can read it off those charts.
To help find ways to improve on the strategy, let's start with your strategy's portfolio payoff matrix equation.
$$ F(t) = F_0 \cdot (1 + \bar r)^t = F_0 + \sum (H \cdot \Delta P) - \sum (Exp) = F_0 + n \cdot \bar x $$
You fixed \( n \cdot \bar x \) to some decaying constant per period and therefore will have your CAGR slow down with time. This made your strategy have a linear return \(\approx (1+rt) \) instead of compounding \(\approx (1+r)^t \). It does not seem like much over the short-term, at the end of year one they give the same answer. But, over the long term, it will make quite a difference.
Your strategy has set \( \Delta (n \cdot \bar x) / \Delta t \) to a gradually decreasing number since \( \bar x\) is decreasing. Saying the profit generation's cruising speed is slowing down. And because of this, you will see a decaying CAGR not just as if the Law of diminishing returns was kicking in but also due to some of it camouflaged in the trading procedures themselves.
The portfolio's payoff matrix equation gives you the answer on what you could do to compensate for the decreasing CAGR. Find ways to increase \(n\), the number of trades, and find ways to increase \( \bar x \), the average net profit per trade. It means that you will have to apply a positive monotonic function to the payoff matrix \( \sum (\gamma (t) \cdot H \cdot \Delta P) \). This will have \( \Delta (n \cdot \bar x) / \Delta t \) increase with time.
It will compensate for the return degradation and give you back your CAGR. I wrote a piece on that subject last year. See, for instance, the link below where that problem was made more explicit with charts:
Hope it helps.