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The question of an edge

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@kevinkdog Ive had a brief look at your AMA thread and you are primarily focused on algo systems. I don’t know much about your world so correct me if Im wrong but that requires a completely different mindset to day trading? You will have a variety of different strategies simultaneously working across multiple timeframes and instruments? You closely monitor expectancy and adjust your ‘worker bees’ when one underperforms? Expectancy is the single most important thing to you because it is the only way to gauge the performance across tens or hundreds of concurrently running algos?

I see now the importance of expectancy however as a day trader I just cannot accept that expectancy is at the top of my hierarchy of needs. Day trading requires that we constantly monitor ourselves and every possible direct and indirect variable that could affect our decision making ability. The food we eat, how much sleep we have had, what mood we are in, money mgmt, FOMO prevention etc..all contribute to how effectively we can execute our trade plan at go time. You can be amazing one day and the next day it’s all gone to shit because one of the parts where discipline slipped spiralled out of control causing a chain reaction. Think Jesse Livermore,

A day trader must monitor all aspects of themselves at all times just the same way that you monitor your algos expectancy at all times. In my world there is not one thing whos importance overrides all else. Expectancy will tell me how well Ive done to date but tomorrow is another day and if Im disciplined in all areas of my approach then I can maintain a positive expectancy.

For the benefit of anyone reading this, who like me was clueless to this very important metric (but thankfully am now fully aware of it), I have been doing some reading and the following is a basic intro on what you need to do. You should try and get as big a sample size as possible, a few hundred trades minimum if you can.

You only need 4 pieces of information:
1. number of winning trades
2. number of losing trades
3. amount of money won
4. 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

Here is an example:
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 $ 0.61% risked = 55% x (1,5 + 1) – 1 = 38%
Or, alternatively, expectancy per $ 0.61% risked = $16.200,00 / $80,00 / 540 = 0,38

Credit to JasperForex on tradingView for allowing me to reproduce the breakdown above.

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