Knowing your trading system’s reward/risk ratio is the cornerstone of it’s effectiveness.
Let’s say you close 75% of your positions for a profit, but your winning positions achieve a $1000 net gain while your losing positions result in a $3000 net loss. If this occurs, you won’t be a profitable trader. Let’s say you close 75% of your positions at a profit, and your winning trades result in a $1000 net gain while your losing trades result in a $1000 loss. If this occurs, your reward/risk ratio will be 1:1 while 75% of your trades are profitable: you will be a profitable trader.
A reward/risk ratio of 2:1 is advised for novice traders. When implementing a 2:1 ratio, two losing-trades will be negated by one winning trade. Conversely, one winning trade will be negated by two-losing trades. In theory, you can lose 50% of your trades and still be a profitable trader. It isn’t necessary to adhere to this ratio and your personal reward/risk ratio should appease your preferences. However, we must consider trading as a numbers game. If you profit from 50% of your trades, and your reward/risk ratio is 1:1, your losing trades are equal to your winning trades, you will not be a profitable trader.
Many traders consider a 1:1 reward/risk ratio to be suboptimal, but we must account for win-rate. If 70% of your trades are winners with a 1:1 reward/risk ratio: you will be a profitable trader. Let’s say you construct two trading strategies, the first strategy has a 70% win-rate when using a 1:1 reward/risk ratio, and the second strategy has a 40% win-rate when applying a 2:1 reward/risk ratio. Both strategies are theoretically profitable, however the 1:1 strategy has a better edge.
That said, some systems/strategies only work when applying a particular reward/risk ratio. This isn’t necessarily a good thing; you want your strategy to be overwhelmingly profitable, but it can be exploited for a profit.
Most strategies with a wider reward/risk ratio have low win-rates. Why? These strategies tend to cut losers short and maximize profits. For example, a 3:1 reward/risk strategy can be profitable with a 30% win-rate.
Assume the strategy loses $1000 on each losing position (7/10 times), and makes $3000 for each winning position.
(0 – 1000) x 7 = -$7000
(0 + 3000) x 3 = +$9000
Hypothetically, every 10 trades closed results in a $2,000 net profit.
Many novice traders value win-rate (# of trades closed for a profit) over any other statistic. A high win-rate is appealing and immediately satisfying; many positions are closed for a profit, your portfolio experiences consistent gains, etc.
A high win-rate system can be consistently implemented for a profit; however, they require additional precautions.
If your losers substantially exceed your winners, a high win-rate adds little value to your system. If your winners achieve a $1500 profit, and you win 70% of the time, but your losers results in a $3500 loss, and you lose 30% of the time: you won’t be a profitable trader. It might sound bizarre to let a bad trade remain open for so long, but, many deceptively high win-rate systems use this approach, and that’s why they have a high win-rate. The system calls for staying wrong until it’s right. Prices could move 20% against your position, but the strategy doesn’t generate a stop-loss signal. Months later that position is still open. Eventually, the profit-target is achieved, let’s say a 2% gain, and the position technically improves the win-rate. These systems aren’t conducive to profitability. If you’re swing-trading/ day-trading or trading a shorter- time frame in general: it’s better to quickly admit defeat and exit a position than hold it until it’s right.
You can assess the efficacy of these systems/strategies by evaluating the average number of sessions a position is held before closed. If the system has a high win-rate with a lot of trades executed, but the average number of sessions in a position is 50+, the system likely suffers from the “holding until right” illusion.
When a system has a high win-rate and a low average number of sessions in a position – it likely has a good edge and is feasible. However, most high win-rate strategies capture quick, modest profits. It’s difficult to contrive a high average gain on winning positions strategy with a high win-rate and a lot of trades executed.
That said, high win-rate strategies with a low holding period complements a conservative options strategy. It’s best to risk a fixed amount and not focus too much on exponential growth.
Let’s look at an example.
Figure 1.1 Backtesting a High Win-Rate Strategy
Our sample strategy executed 319 trades since 1986. 240 trades were closed for a profit, and 79 trades were closed for a loss. As a result, 75.24% of our positions were profitable. Our average winning position resulted in a 2.47% gain in 6 sessions, while our average losing position resulted in an 6.02% loss in 9 sessions. Our strategy was overall profitable due to the win-rate; our average loss was much greater than our average win. This is due to our profit-taking/stop-loss criteria, which is relatively aggressive.
In theory, this strategy could work well with a conservative options strategy; our ROI on winning positions would improve. For example, if a 2.47% increase in 6 sessions results in a ~100% profit, and the most we can lose on a long call/put is 100%, then our strategy would incorporate a 1:1 ratio with a ~75.24% win-rate. Keep in mind the backtest accounts for past performance, we can’t execute trades with the speed of a computer, and it’s unlikely we could precisely replicate this past performance. A strategy such as this will blow up if you commit too much capital to one position.
Let’s say we have a portfolio with $10,000 buying power, and are risking $135 on a long call for each each trade in Figure F.4. For the sake of example, we will not trade a percentage amount of our total trading capital, but a fixed dollar amount. There are numerous factors to account for; we can’t forecast potential profitability without looking at the options chain for each trade setup. The average winning position captures a 2.47% upswing in 6 sessions. Our winning trades will likely see a nice ROI with adequate contract selection.
Figure 1.2 Hypothetical Net-Profit For a Position
Figure 1.2 is simply an example of what ROI could be if a buy/sell signal were generated tomorrow and we opened a contract expiring in 30 days at the strike price directly above the average winning position price move. The hypothetical ROI if everything worked perfectly, which is unrealistic to expect, would be 85%. We shouldn’t make any inferences until evaluating the strategy’s performance on most recent historical data. For now, let’s account for slippage and assume each trade nets 75% ROI. Again, there are numerous factors to account for: theta, delta, vega, gamma, IV, current stock price, bid/ask spread and liquidity, premium, etc.
We are risking $174 per trade, so we are earning 130.5$ per winning trade. If we remain consistent in our approach, our hypothetical gross profit on winning positions totals $31,320 over the period. The average losing position resulted in a 7% loss, and the largest losing position resulted in a 14% loss. Assuming we lose 100% of invested capital, which probably wouldn’t happen on all losing position, we are limited to a $174 loss- the initial investment. We lost 79 trades for a total of -$-13,746, achieving a net gain of $17,574 over the performance tested period. The potential profit for a long call is theoretically infinite. We can reasonably expect to make larger gains on winning positions experiencing a stronger price move than the average winning position, and our maximum loss is still restricted to $174. For example, the largest winning position captured a 9.09% price move. Our strategy automatically closes a winning position when a 2% profit is achieved. The 9% price move likely happened from one session’s close to the next session’s open. The largest losing position resulted in a -16.79% loss. Normally, this would be disastrous for one trade. However, our loss was limited to 100% of our invested capital – $174. We aren’t accounting for IV or theta; a 2% winning position when holding shares, but takes weeks to achieve, will likely be a losing contract. Everything discussed should serve as an example, not an expectation.
The performance test results reflect traditional share buying/selling, to which the sample strategy achieved a net profit. In addition, you could implement more prudent stop-loss criteria to diminish the percentage loss on losing positions.
Let’s look at an example of a low win-rate strategy that achieved a net profit.
Figure 1.3 Low Win-Rate Profitable Trading System/Strategy
Figure 1.3 shows performance test data from 1980 – 2022.
Our sample strategy closed 64 positions for a profit while 104 positions were closed for a loss; a 32.16% win-rate was achieved. Such a low win-rate may seem intuitively unprofitable. However, the average winning positions far exceeded the average losing position. The average winning trade achieved a 15% profit while the average losing trade achieved a 5.68% loss. The largest losing positions amounted to a 25% loss. The largest winning position achieved a remarkable 100% profit. The 25% loss probably occurred due to split adjusted data. If a position was opened at $0.04 cents and closed at $0.03 then we lose 25% of our invested capital. We can assume this to be true because our stop-loss criteria is actually quite prudent.
How did we achieve profitably and a 100% ROI trade with a 32% win-rate? Our strategy emphasized letting winners run and cutting losers short. If a double crossover occurs, we remain in the position until an inverse double crossover occurs. Vigorous momentum will keep us in a trade until it exhausts for a transient period. If a robust trend does not occur, an inverse double crossover will quickly occur and our position will be closed.
If the strategy’s hypothetical net profit were caused by the 100% ROI trade, it would be imprudent to consider the strategy viable.
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