9 Lessons I’ve learned While Quant Trading For The Past 1 Year | Hacker Noon

June 21st 2020

Author profile picture

I’m approaching one year since diving full time into quant trading. My business a year ago wasn’t performing too well, and I was hoping for more control over returns — especially for a more predictable ROI. That’s how it all started.

I didn’t expect this journey to be as challenging as it’s been — looking back at all the learning, re-learning, programming, re-programming, testing, re-testing, and launching strategies at some point, only to see them fail. However, there are a few strategies that make it through all the processes and become profitable. These winning strategies have some common patterns, which I tried to compile into the following lessons that I have learned over the past year.

Some of the points may appear obvious to you, as a more experienced trader. For me, each individual one was an enormous insight, sometimes followed by a big shift in how I approach the markets. I wish I had known these points beforehand, which might have saved me countless hours. The following lessons are addressed to me and in no meaningful order.

Strategically pick your markets

Trading US stocks, forex, and bonds probably is a bad idea. It is not the wisest choice, due to too much competition with the biggest players. Find your liquidity sweet spot by taking a look at markets that would support your liquidity needs; however, they shouldn’t be magnitudes larger. Play and win in niche markets by learning their rules, rather than trading where big players trade and where the game is much harder. My point is this: a strategy on Philippines stocks likely will be more profitable than the same one on US stocks.

Learn the rules and accept them

I traded a few different markets (in hindsight, I should have stuck with one). Each has different rules and is rigged in its own way. Market makers (or the most dominant players in one market) do everything to win. Assume the markets are rigged, learn the rules, and play by them, but don’t deny them by thinking markets act naturally. Don’t try to “outsmart” the markets; this likely will backfire. Look for traces (behavioral, spoofing, placed orders, and liquidity hunts) that big players leave, and use them for your advantage.

Know your priorities

There’s so much to do in quant trading: strategy development, optimization, backtesting, execution, and risk management. Don’t focus on the wrong things in the beginning — like optimizing parameters. Rather, build very basic MVP versions of each part in the equation and optimize by iterating while in production. A perfectly optimized strategy won’t help if the execution part doesn’t work correctly.

Expect to lose in your first year

Don’t start scaling as soon as you see some initial success, as it may wipe out big parts (40% in my case) of your portfolio. It will take you much more effort to make it back; instead, it’s easier to adapt proper risk measures in the first place. By having an expectation to lose (the first year at least), you won’t be tempted to put more capital than necessary into testing and learning.

Don’t rush with capital, rush with execution

I was too quick in scaling up capital without thinking about risk. On the contrary, I often found myself in analysis paralysis, promising myself to launch a new strategy after “just one more optimization”. I was over-optimizing too much. I should have just launched multiple strategies to see what works first, then optimizing in an ongoing way. Building and optimizing strategies based on theory doesn’t help, if there’s no practical feedback.

Don’t use price stops

I found there are two ways price stops can be used: either not at all or to protect against black swan events (99.9th percentile of the volatility distribution). Instead of price stops, use time stops and proper position sizing. Price stops will, as research shows, destroy a good strategy, simply due to randomness in volatility. The time dimension is much more manageable and predictable than the price dimension of a hypothesis expressed by your trade (both in backtest and in live trading). By using time stops, you are setting a time constraint in terms of how long your hypothesis is valid, which almost always reduces variance (and increases Sharpe ratio).

Know entries and exits

For each trade, know where to enter and where to exit. For me, these are set based on two rules — one being a modified formula of Average True Range. It’s almost a requirement to have pre-defined rules for entries and exits, in order to properly backtest and to know what to expect in live trading.

Know your numbers

For each strategy, you must know the expected value, hit rate, expected drawdown, longest drawdown, expected volatility, variance, Sharpe ratio, standard deviation of returns, skewness of returns, and value at risk. Also, proper bet sizing, risk of ruin, Kelly fraction, and optimal F should be strategically chosen based on how the strategy performs during the backtest.

Make risk management a priority

Wiping out 40% of capital might happen in a day; however, making it back can take many months — if not years. Use proper risk management in the first place, and be aware of the potential risk of ruin due to black swan events. It’s always a good idea to expect the worst case. It shouldn’t be a challenge for your strategies to wake up one day to a -50% market.


The Noonification banner

Subscribe to get your daily round-up of top tech stories!

read original article here