Quantitative analysis: what it is and why you need to do it

quotesI think most traders and investors understand that we live in a world in which markets are becoming ever more competitive. To make money in those markets, you must have an edge, and you have to truly understand your edge. But the question remains: where and how do you find an edge and how can be you sure you understand it? I want to share a little bit of my own experience in this regard, and give you some ideas for doing your own work.

At the beginning, I think it’s important to understand the questions we are asking. No matter what your trading/investment methodology is, you are assuming that something is more likely to happen than something else, based on whatever tools you are using. Think about that a minute, because some people are confused on this point. Whatever you do to decide when to get in and out of markets, you are doing so because you think one outcome is more likely than another. If we don’t have this understanding, then we can easily fall into a mindset that says “x must happen”, “x will happen”, or “x has to happen.” That way of thinking is dangerous; at best, x is a little bit more likely to happen. So, how do we know that is true?

Quantitative analysis and quantitative tools can seem to be intimidating, but, at their core, they are very simple–all we’re doing is looking at a bunch of things that happened in the market, defining some conditions, and seeing if the those conditions have been tied to certain outcomes. We then make a (hopefully small) leap and assume, if we’ve done our work right, that seeing these conditions in the future will make certain outcomes more likely. Here are some concrete examples of statements we can test. Notice that they each include a condition or set of conditions, and an outcome: Stocks with good earnings stability are likely to go up. Stocks making 52 week highs are likely to go up. After a decline in price, if volatility contracts, stocks are more likely to go down. After making new 20 day highs, a commodity is more likely to continue to go higher. (By the way, some of those statements might be true, and some might be false. They are simply examples.)

How do we answer those questions? Well, there are some semi-sophisticated techniques that you may have encountered in a math class a long time ago like linear regression or principal component analysis. These tools do have their place, but we don’t need them for much of work. We can do some pretty deep analysis with these steps:

  • Get a bunch of market data together. We need to make some decisions about what timeframe (daily, weekly, 1 minute?), what markets, what time span (recent? 5 years? 50 years?) we want to cover. This stage of getting and managing our data is harder than you might think because we have some thorny issues like dividends and splits for stocks and rolls for futures to consider. In addition, nearly all data sources have some errors, so we’re going to need to spend (too much of our) time cleaning and wrangling this data.
  • Define a set of conditions. These conditions can include price patterns, other technical factors, tools calculated from price inputs (e.g., MACD), fundamental factors, changes in fundamental factors, economic data, macro factors, sentiment data, etc. The only serious caveat here is that these conditions need to be defined precisely because we are going to test them over hundreds or thousands of occurrences.
  • Look for every time the condition occurred in every market we are analyzing.
  • See what happened following every occurrence of the conditions.
  • Compare what happens after the conditions to all other market data. What we’re looking for here is some evidence that our condition set has the power to influence markets.

That’s really it, and it’s not so intimidating: define a condition; test it, and then look at the results. We can do this with pencil and paper, and, indeed, that is how I started. But, to really do this work, we need good tools. In my next post I’ll share some of the tools I have developed and used over the years, starting with that pencil and paper, through Excel spreadsheets, to convoluted combinations of different tools, to, finally, a robust framework written in Python. I’ll tell you why I think Python is the right choice for much of this work and for many of my readers, and then we’ll dig a lot deeper into what you need to know to use it well.


Adam Grimes has over two decades of experience in the industry as a trader, analyst and system developer. The author of a best-selling trading book, he has traded for his own account, for a top prop firm, and spent several years at the New York Mercantile Exchange. He focuses on the intersection of quantitative analysis and discretionary trading, and has a talent for teaching and helping traders find their own way in the market.

This Post Has 4 Comments

  1. Sunil Reddy

    Thanks Adam for an another insightful post. Looking forward to your next post to learn more about the tools that you have developed & used over the years.

  2. steven macdonald

    Hello Adam, I have been reading your blog and listening to your podcasts (which reminds me when are you making more?). I have been “trading” for around 3 years and have had some massive ups and equally downs in that time. It took me years to even place a trade as I consumed all the information available and built my own judgements on what might work and what wouldn’t. I had built a strategy and back-tested both demo and actual back testing prior to even contacting a mentor group.

    The mentor group was great from a motivational point of view but they disliked my self made strategy and proceeded to pick it apart. The strategy wasn’t bad just didn’t fit into there methods and I assume gave them a showing up when I made 12% on my 3rd month with them as there strategy struggled to break even. The main mentors placed massive doubts into my mind with comments that it was unsustainable and that I had no bases for it working. I was pushed into increasing R/R and pushed into an aggressive risk profile. I jumped from risking 0.25% to 2.0% within 6 months. The manipulation finally paid off as the strategy took two straight months lose off 10%. I was left wondering what had gone wrong the numbers were sound and the planning was millimetre perfect. I took a month off the mentor program and spent it buried in paper work looking over trades and re-analysing every move I had made. It was at this point I notice the original strategy looked nothing like the mess I was trading now. I was pushed to have more concern about fantastic r/r than about the actual trade. My psychological state of mind was in a death spiral. I contacted the mentors and informed them I would not be returning.

    For the past year I have been using the experience to re-build my strategy and to better understand its fundamentals.
    All that aside one thing I have found massively helpful has been a word document filled with screenshots of recent successful trades, Before I ever take a trade I compare with the collection of screenshot, does the set-up match one in the past or does it have similar characteristics.

    Another useful habit is keeping all paper work relating to a strategy, it can be helpful to refer to original designs to see how the strategy has evolved. Create a new Strategy plan every year so you have a reference point for when a change happens

    So the above story kind of ties in why keeping previous information can help you assess and see what has changed, without the reference material I would still be sitting wondering what went wrong.

    Kind Regards

    1. Max Schuette

      It sounds like you’re doing very good work. If I understand correctly, you have now forward tested your strategy and it is working? if so you should implement it and trust. If not, look at the market conditions for testing. Look at the market conditions since testing, and notice what is different. Look from multiple time frames. Trust yourself.

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