Introduction
Traders want to win. Nothing else matters to them; and they think and believe the most important question is timing the entry. Exits don’t matter at all, because if they time the entry, they could easily get out long before a retracement erases their profit. O so they believe.
That’s the reason there are thousands of books about Technical Analysis, Indicators, Elliott Wave Forecasting, and so on, and just a handful of books on psychology, statistical methods, and trading methodology.
The problem lies within us, not in the market. The truth is not out there. It is in here.
There are a lot of psychological problems that infest most of the traders. One of the most dangerous is the need to be right. They hate to lose, so they let their losses run hoping to cover at a market turn and cut their gains short, afraid to lose that small gain. This behavior, together with improper position sizing is the cause of failure in most of the traders.
The second one is the firm belief in the law of small numbers. This means the majority of unsuccessful traders infer long-term statistical properties based on very short-term data. When his trading system enters in a losing streak, they decide the system doesn’t work, so they look for another system which, again, is rejected when it enters in another losing sequence and so on.
There are two problems with this approach. The first one is that the trading account is constantly consumed because the trader is discarding the system when sits at its worst performance, adding negative bias to his performance every time he or she switches that way. The second one is that the wannabe trader cannot learn from the past nor he can improve it.
This article is a rough approach to the problem of establishing a trading methodology.
1.- Diversification
The first measure a trader should take is:
- A portfolio between 3-10 of uncorrelated and risk-adjusted assets; or
- A portfolio of 3 to 5 uncorrelated trading systems; or
- Both 1 and 2 working together.
What’s the advantage of a diversified portfolio:
The advantage of having a diversified portfolio of assets is that it smooths the equity curve and, and we get a substantial reduction in the total Drawdown. I’ve experienced myself the psychological advantage of having a large portfolio, especially if the volatility is high. Losing 10% on an asset is very hard, but if you have four winners at the same time, then that 10% is just a 2% loss in the overall account, that is compensated with, maybe, 4-6% profits on other securities. That, I can assure you, gave me the strength to follow my system!.
The advantage of three or more trading systems in tandem is twofold. It helps, also improving overall drawdown and smooth the equity curve, because we distribute the risk between the systems. It also helps to raise profits, since every system contributes to profits in good times, filling the hole the underperforming one is doing.
That doesn’t work all the time. There are days when all your assets tank, but overall a diversified portfolio together with a diversified catalog of strategies is a peacemaker for your soul.
2.- Trading Record
As we said, deciding that a Trading System has an edge isn’t a matter of evaluating the last five or ten trades. Even, evaluating the last 30 trades is not conclusive at all. And changing erratically from system to system is worse than random pick, for the reasons already discussed.
No system is perfect. At the same time, the market is dynamic. This week we may have a bull and low volatility market and next one, or next month, we are stuck in a high-volatility choppy market that threatens to deplet our account.
We, as traders need to adapt the system as much as is healthy. But we need to know what to adjust and by how much.
To gather information to make a proper analysis, we need to collect data. As much as possible. Thus, which kind of data do we need?
To answer this, we need to, first look at which kind of information do we really need. As traders, we would like to data about timing our entries, our exits, and our stop-loss levels. As for the entries we’d like to know if we are entering too early or too late. We’d like to know that also for the profit-taking. Finally, we’d like to optimize the distance between entry and stop loss.
To gather data to answer the timing questions and the stop loss optimum distance the data that we need to collect is:
- Entry type (long or short)
- Entry Date and time,
- Entry Price
- Planned Target price
- Effective exit price
- Exit date and time
- Maximum Adverse Excursion (MAE)
- Maximum Favourable Excursion(MFE)
All the above concepts are well known to most investors, except, maybe, the two bottom ones. So, let me focus this article a bit on them, since they are quite significant and useful, but not too well known.
MAE is the maximum adverse price movement against the direction of the trend before resuming a positive movement, excluding stops. I mean, We take stops out of this equation. We register the level at which a market turn to the side of our trade.
MFE is the maximum favourable price movement we get on a trade excluding targets. We register the maximum movement a trade delivers in our favour. We observe, also, that the red, losing trades don’t travel too much to the upside.
Having registered all these information, we can get the statistical evidence about how accurate our entry timing is, by analysing the average distance our profitable trades has to move in the red before moving to profitability.
If we pull the trigger too early, we will observe an increase in the magnitude of that mean distance together with a drop in the percent of gainers. If we enter too late, we may experience a very tiny average MAE but we are hurting our average MFE. Therefore, a tiny average MAE together with a lousy average MFE shows we need to reconsider earlier entries.
We can, then, set the invalidation level that defines our stop loss at a statistically significant level instead of at a level that is visible for any smart market participant. We should remember that the market is an adaptive creature. Our actions change it. It’s a typical case of the scientist influencing the results of the experiment by the mere fact of taking measurements.
Let’s have a look at a MAE graph of the same system after setting a proper stop loss:
Now All losing trades are mostly cut at 1.2% loss about the level we set as the optimum in our previous graph (Fig 2). When this happens, we suffer a slight drop in the percent of gainers, but it should be tiny because most of the trades beyond MAE are losers. In this case, we went from 37.9% winners down to 37.08% but the Reward risk ratio of the system went from results 1.7 to 1.83, and the average trade went from $12.01 to $16.5.
In the same way, we could do an optimization analysis of our targets:
We observed that most of the trades were within a 2% excursion before dropping, so we set that target level. The result overall result was rather tiny. The Reward-to-risk ratio went to 1.84, and the average trade to 16.7
These are a few observations that help us fine-tune our system using the statistical properties of our trades, together with a visual inspection of the latest entries and exits in comparison with the actual price action.
Other statistical data can be extracted from the tracking record to assess the quality of the system and evaluate possible actions to correct its behaviour and assess essential trading parameters. Such as Maximum Drawdown of the system, which is very important to optimize our position size, or the trade statistics over time, which shows of the profitability of the system shrinks, stays stable or grows with time.
This kind of graph can be easily made on a spreadsheet. This case shows 12 years of trading history as I took it from a MACD trading system study as an example.
Of course, we could use the track record to compute derived and valuable information, to estimate the behaviour of the system under several position sizes, and calculate its weekly or monthly results based in the estimation, along with the different drawdown profiles shaped. Then, the trader could decide, based upon his personal tolerance for drawdown, which combination of Returns/drawdown fit his or her style and psychological tastes.
The point is, to get the information we must collect data. And we need information, a lot of it, to avoid falling into the “law of small numbers” fallacy, and also to optimize the system and our risk management.
Note: All images were produced using Multicharts 11 Trading Platform’s backtesting capabilities.
