Modeling a Trading Plan for the New Year
People often think that the main benefits of working for a proprietary trading firm are capital and a favourable cost structure. But what’s easy to overlook is the fact that you don’t just get handed a massive trading account right off the bat. You need to prove your worth on sim and then small size before you are deemed ready for that.
But I’d argue that it’s the structure that helps traders to find and sustain success that’s of the greatest benefit to prop traders.
As we approach the end of one trading year and begin looking forward to the next, we are presented with an ideal opportunity to unburden ourselves of past baggage, jettison useless tools, and start planning anew. And yet, to embark successfully on the journey ahead, a trader must first adequately reflect on the recently completed trek, taking stock of what went right, what went wrong, and how to craft a plan with adequate contingencies for going forward into uncertain conditions.
As traders, we need a proper framework to measure our results against. Otherwise, we are liable to grope in the dark, reaching out here and there but with little idea of whether we are making any forward progress. As such, I like to take this time of the year to begin sketching out a quantitatively focused trading plan for the year ahead. This forecasting is then distilled into a base case scenario, accompanied by pessimistic and optimistic cases, all directly constructed upon the foundation of my past trading statistics—with the requisite margin of error around such expectations.
Since the market environment, volatility regime, and economic landscape are constantly changing, prudent traders cannot expect past performance will translate perfectly to future results. The preceding year may have been one of bullish trending risk-on markets, perhaps ideally suited (or not) to the trader’s momentum-based strategy, whereas the coming year ahead could surprise participants with ranging markets of either high or low volatility. As such, a simple extrapolation of results from last year(s) is naïve. But that doesn’t mean that planning is not needed or helpful. In fact, it is imperative to measure progress. However, we need to incorporate a broader range of expectations and sensitivity analyses. As the year unfolds, we can then begin to calibrate our plan more appropriately to the developing environment. As always, each trade outcome itself is largely unpredictable, but if the trader can adhere to some basic procedural rules regarding their strategy—risk control, stop limits, profit-taking rules, etc., trusting in their process—then predictability over time becomes much more of a reality than one might initially assume.
So, what kind of ground-level statistics are we working with when building this kind of forecast? My advice is to stick to the core basics that drive results and expectancy. And you don’t need sophisticated programming experience. But you do need some basic spreadsheet capabilities like those found in Excel (this is how most Fortune 500 companies build forecasting models, so it should be suitable for an individual trading business).
Now given these elements, let us assume that a trader’s average win rate last year was 50%, but their worst month was a mere 25% win rate and that their best month was a solid 75% win rate. Furthermore, let us assume that the trader’s average R-factor per trade was 2R, but their worst month in terms of payout was a mere 0.25R, while the best month was 6R. These parameters, along with the number of trades taken per period, average risk per trade, expectancy per contract, etc., will allow one to plan for a range of potential process outcomes. It is important to note, however, that you shouldn’t assume that your past results contain either your worst or your best performance metrics, so be flexible, realistic, and open-minded in your scenario planning on both ends of the spectrum—optimistic and pessimistic. By applying various combinations of these factors as derived from one’s actual statistical history, the trader can begin to approach a central tendency for expectations. These expectations can then help guide the trader, offering an objective benchmark to track results against and afford a view into the potential profitability of one’s trading business for the year ahead.
And to be clear, this is no different than any other business forecasting. Why should it be? If I’m serious about my trading business, I should be treating it as such and not just winging it every day, buying double the amount of flour on Friday for my bakery simply because I feel that Monday I’ll sell a lot of bread. That’s ridiculous! I need to plan costs and revenues for the entire month, quarter, and year, then measure my results relative to those scenarios as the year progresses. This is what is meant by a focus on the “process”, not individual outcomes.
One final parting note is that the development of quantitative metrics to guide decision-making is essential. Qualitative factors are of obvious importance, but they often lack objectivity—more prone to subjective bias—and tend to be more challenging to track and measure. Clear quantitative metrics afford more objective measures of progress. This is your business—plan, track, measure, adapt and repeat; constantly evolving, continuous improvement.
– Joe Abboud (@TendexCapital, Head Trader at Convergent Trading)
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