Money Management for High Probability Systems

I spent last weekend overviewing trading systems with my favorite money management software. Normally, when traders think of a great trading system, they expect fantastic results like 80% winners and every trade has to make $2 for every $1 that it loses.

The edge in real trading systems typically falls wildly short of that goal post. Rather than convince you with software, try running the numbers in your head.

Say that we do 100 trades with a $100 account balance. 80% of them win and make $2, which is $160 in profit. 20% of them lose $1, which is $20 in total losses. Now ask yourself, “Does it sound plausible to earn a 40% return after only 100 trades?”

The answer to the question probably correlates with your trading experience. If you answered yes, then I also have some oceanfront property to sell you in Arizona. Experience should make it clear that such numbers are fairy-tale optimistic.

Las Vegas makes billions of dollars every year with a total edge of 0.5%. You don’t need to hit grand slams to take money out of the markets. Little base hits will get you there, too.

What is high-probability

At the risk of moving the goal posts, I define high probability as anything above 50%. Many traders comes across the word “high probability” and start drooling over 80-90% winners.

Remember that winning percentage isn’t everything. If we play a game where you win 99% of the time but lose $200 when you lose, you’d quickly figure out to not play. The name of the game is to walk away with a profit over time. It doesn’t matter how you get there.

A high probability, one which exceeds 50%, offers the unique advantage that you can aggressively use money management to enhance returns. High probabilities also come with the unique property of offering long streaks of consecutive winners. Consecutive losing streaks are comparatively rare.

Money management in a real strategy

One of my clients trades a strategy with 60% winners. The ratio of the average winner to the average loser. That is, the average winner earns $1. The average loser loses $1.

Because it wins 60% of the time, that means it makes $60 for every $40 it loses, which is a profit factor of 1.5. Not bad.

A strategy with this property expects to earn a return of 40.9% after 200 trades if it risked 1% of the original account balance per trade.

The strategy using fixed dollar risk expects to make a 40.9% average return after 200 trades.

Making a minor change so that the strategy risks 1% of the current balance per trade makes a huge difference in the average return. It jumps nearly 10% to an average return of 50.7%.

Fixed fractional money management is the idea of keeping the risk proportional to the current balance. Using a $10,000 account as an example, a trade risking 1% of the balance risks $100. If the account balance increased to $11,000, each trade now risks $110. The dollar risk grows with time while the percentage risk remains unchanged – hence, it’s fixed fractional.

A fixed fractional money management approach increases the average profit to 50%.

A unique money management idea

Remember how I mentioned that high probability systems are prone to streaks of winners and losers? Check out what happens if you increase the risk after each winning trade by 10%.

The median outcome gets pulled up enormously. The average isn’t jumping to 63% because of a handful of massive returns. Instead, almost every series in the test experiences a huge increase in performance.

The average balance after 200 trades jumps yet another 12% for a 63% return. The only disadvantage is that the worst case scenario dropped from a 6.8% loss to a 10.3% loss.

Increasing the risk by 10% from the previous trade increases the average return to 63% and the median return to 58%.

Applying the rule so that consecutive winners only increase their risk when the balance is greater than the starting balance makes the worst case scenario slightly less bad. It comes at the price of a slightly lower average and median return.

Applying the consecutive winning trades rule only slightly helps the worst case scenario. It comes at the expense of a lower average and median return.

Conclusion

You’d be a fool to stick with fixed risk if your system offers offers high probability winners without huge losing trades. Simple steps like fixed fractional money management can substantially increase profits. Applying a trick like increasing the risk by 10% after each consecutive winner offers another huge performance boost.

The average outcome started at a healthy 41%, which is already stellar. Applying the simple techniques outlined here increased the average performance after 200 trades to a 63% return. That’s some world class trading.