If you know the expectancy of your trading strategy, you will be able to deal with these situations better. There is a psychological aspect here: knowing the predictable profitability of a larger number of trades you undertake will build your confidence, which in turn reduces your tendency to shortcut winners and to let losers run too long. Having this confidence will thereby improve your overall results. In December I developed a spreadsheet for myself, linked to my trading records, where I calculate several performance indicators, among which expectancy.
How to determine the expectancy of your trading system? Assuming you keep records of your trades, you should go back and look at all your trades that were profitable versus all your losing trades. Do this over a period of at least 3 months and at least 100 trades. The more data you can use, the more accurate the result. We only need 4 pieces of information: number of winning trades, number of losing trades, amount of money won and amount of money lost. From this data we can calculate the following:
Net profit = amount of money won - amount of money lost
Win rate = number of winning trades / total number of trades
Lose rate = 1 - win rate
Average winner = amount of money won / total number of winners
Average loser = amount of money lost / total number of losers
Average reward / risk = average winner / average loser
Expectancy per trade = win rate x average winner – lose rate x average loser
Or, alternatively, expectancy per trade = net profit / total # trades
Expectancy per month (profit forecast) = expectancy per trade x average # trades per month
Expectancy per amount of money risked = win rate x (average reward / risk + 1) – 1
Or, alternatively, expectancy per amount of money risked = net profit / average loser / total # trades
I will illustrate this with an example for a euro account. Lets assume we have been trading for 6 months and made a total of 540 trades. 297 of them were profitable and 243 were not, with €35.640,00 profit coming from the winning trades and €19.440,00 loss stemming from the losing trades. Lets make the calculations:
Net profit = €35.640,00 - €19.440,00 = €16.200,00
Win rate = 297 / 540 = 55%
Lose rate = 1 - 55% = 45%
Average winner = €35.640,00 / 297 = €120,00
Average loser = €19.440,00 / 243 = €80,00
Average reward / risk = €120,00 / €80,00 = 1,5
Expectancy per trade = 55% x €120,00 – 45% x €80,00 = €30,00
Or, alternatively, expectancy per trade = €16.200,00 / 540 = €30,00
In our example the expectancy per trade is €30,00. This means, on average (over many trades), each trade will contribute €30,00 to the overall P&L.
Expectancy per month = €30,00 x 540 / 6 = €2.700,00
In our example we can forecast a monthly profit of €2.700,00 based on prior performance.
Expectancy per € risked = 55% x (1,5 + 1) – 1 = 38%
Or, alternatively, expectancy per € risked = €16.200,00 / €80,00 / 540 = 0,38
In our example the expectancy of the trading strategy is 38%. That means the trading strategy will eventually (over many trades) return 38 eurocents for each euro risked.
Once you know your expectancy, as a function of your own trading statistics, you can forecast how much you could make per week, per month and per year.
Required Reward:Risk Ratio = (1 / Winrate) – 1
Example 2: If your system has a historical winrate of 60%, you need a reward:risk ratio of 0.6 : 1 to achieve a long-term expectancy:
(1 / 0.6) – 1 = 0.7
Question for you. How do you add to the equation a trade that required say a roll forward but eventually was not a loss. Simple example; I sell a put and on the day of expiry I am finding my thesis did not quite pan out yet and I still believe in it so I roll it forward and low and behold my thesis pans out by the next expiry and I'm on positive ground. Now I could have been on + ground before the roll just not where I wanted to be or I could have been -. How do I treat the two different trades. I'm assuming you'd keep the two trades separate in the equation and not treat them as one trade even though mentally I suspect many 'think' about them as one play. Thanks again.