9 ms·

Changing a mean reversion strategy to deliver 30% annual returns since 1999

17 points by carlossouza 2y ago

OutOfHere 2y ago
I think the risk of a 35% drawdown is too big with this strategy. Even if 20%, it's still too big. Perhaps 15% would be about at the borderline of okay.
- dbs 2y ago
  No matter how you minimize drawdown from a backward perspective, there is a reasonable chance there will be an event in the future where you will have a 50pct drawdown. Many of these strategies stopped working in 2008 because the markets became too crowded with players exploring them. Especially the ones that have low drawdowns attract a lot of competition. The writing was already in the wall with the quant bloodbath of 2007.
- mmarian 2y ago
  Just bear in mind the S&P 500 had a drawdown of 60%, over 13 years, from 2000 to 2013 :)
  - OutOfHere 2y ago
    While I entirely agree in spirit and in context, and I get the point too, the historical specifics as clarified by GPT were: During the period from 2000 to 2013, the S&P 500 did experience significant drops, particularly during the dot-com bubble burst in the early 2000s and the financial crisis of 2007-2008. The largest drawdowns in this period were: 1. *Dot-com Bubble (2000-2002)*: The S&P 500 fell significantly after the peak in March 2000, dropping about 49% until it bottomed out in October 2002. 2. *Global Financial Crisis (2007-2009)*: The index again suffered a major drop, losing approximately 57% of its value from its peak in October 2007 to its low in March 2009. However, these drawdowns did not last continuously for 13 years, nor did they result in a cumulative drawdown of 60% sustained over that entire period. The S&P 500 recovered from these lows and even reached new highs within the timeframe specified.
    - mmarian 2y ago
      Makes sense. The reason I highlighted is because I'm finding that most people overestimate how safe some investment strategies are without actually looking at the historical data.
czl 2y ago
Essential reading for all prospective traders is Taleb's "Fooled by Randomness." Markets are full of feedback loops, so you can't expect the same results with "paper" backtesting or "paper" forward testing as with real trades, especially in larger amounts. With such paper testing you can discover and fool yourself with amazing high-probability, high-earning strategies that come with the hidden surprise of low probability catastrophic losses. For example, many naive gamblers think that a strategy with a 45% chance to win, combined with betting to cover losses, will nearly always succeed because the odds of losing ten times in a row seem low. However, when the inevitable 11th loss occurs, it can be devastating.
- OutOfHere 2y ago
  This is true, but traders like me chase it anyway, refining the strategy at each failure. If there is no refinement possible, then the trades should stop.
  - czl 2y ago
    > refining the strategy at each failure You make it sound like you are improving yet with all traders like you "chasing it anyway, refining the strategy at each failure" what exactly are you improving? When you play a game like "rock paper scissors" what refinement is possible? Due to market feedback loops the patterns you look for profits are transient. When they do persist do you ever wonder about them being like fake "tells" in poker that lure you to bet big setting you up to be smashed later? High frequency trading / front running information and arbitrage may be profitable but require low latency costly access. Apart for that my impression is that technical analysis based short term trading (as opposed to long term investing) is on net a negative sum activity. Like a game of "rock paper scissors" there is nothing to improve.
    - OutOfHere 2y ago
      I don't view it as RPS. I view it a bit as a game of chess. Whether it is a net positive or net negative, time will tell. I am not claiming success. Those who are so mentally closed off that they will downvote any differing opinion without understanding it will never succeed with it.
spicyusername 2y ago
What tools and services does one use to do this kind of testing? Where does the data come from?
- carlossouza 2y ago
  The data used is Sharadar Core US Equities Bundle: https://data.nasdaq.com/databases/SFA https://data.nasdaq.com/databases/SFA It's a great survivorship-bias-free dataset. Regarding tools, I use Python. I wrote the backtesting software many, many, many years ago during my Master's degree, and I've been refining it ever since. It's an event-driven engine (they are slower than vector-based engines, but they are easier to write strategies for, understand, and debug) with all the bells and whistles, similar to the late Zipline. In fact, I tried most of the Python backtest engines that exist, and that's why I prefer to use what I built over the years: I have 100% understanding of what’s happening and 100% control. I’m thinking about open-sourcing it… anyway, the logic is not that complicated.
  - spicyusername 2y ago
    Thanks!
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17 points by carlossouza 2y ago

I think the risk of a 35% drawdown is too big with this strategy. Even if 20%, it's still too big. Perhaps 15% would be about at the borderline of okay.

dbs 2y ago
No matter how you minimize drawdown from a backward perspective, there is a reasonable chance there will be an event in the future where you will have a 50pct drawdown. Many of these strategies stopped working in 2008 because the markets became too crowded with players exploring them. Especially the ones that have low drawdowns attract a lot of competition. The writing was already in the wall with the quant bloodbath of 2007.
mmarian 2y ago
Just bear in mind the S&P 500 had a drawdown of 60%, over 13 years, from 2000 to 2013 :)
- OutOfHere 2y ago
  While I entirely agree in spirit and in context, and I get the point too, the historical specifics as clarified by GPT were: During the period from 2000 to 2013, the S&P 500 did experience significant drops, particularly during the dot-com bubble burst in the early 2000s and the financial crisis of 2007-2008. The largest drawdowns in this period were: 1. *Dot-com Bubble (2000-2002)*: The S&P 500 fell significantly after the peak in March 2000, dropping about 49% until it bottomed out in October 2002. 2. *Global Financial Crisis (2007-2009)*: The index again suffered a major drop, losing approximately 57% of its value from its peak in October 2007 to its low in March 2009. However, these drawdowns did not last continuously for 13 years, nor did they result in a cumulative drawdown of 60% sustained over that entire period. The S&P 500 recovered from these lows and even reached new highs within the timeframe specified.
  - mmarian 2y ago
    Makes sense. The reason I highlighted is because I'm finding that most people overestimate how safe some investment strategies are without actually looking at the historical data.

czl 2y ago

Essential reading for all prospective traders is Taleb's "Fooled by Randomness." Markets are full of feedback loops, so you can't expect the same results with "paper" backtesting or "paper" forward testing as with real trades, especially in larger amounts. With such paper testing you can discover and fool yourself with amazing high-probability, high-earning strategies that come with the hidden surprise of low probability catastrophic losses. For example, many naive gamblers think that a strategy with a 45% chance to win, combined with betting to cover losses, will nearly always succeed because the odds of losing ten times in a row seem low. However, when the inevitable 11th loss occurs, it can be devastating.

OutOfHere 2y ago
This is true, but traders like me chase it anyway, refining the strategy at each failure. If there is no refinement possible, then the trades should stop.
- czl 2y ago
  > refining the strategy at each failure You make it sound like you are improving yet with all traders like you "chasing it anyway, refining the strategy at each failure" what exactly are you improving? When you play a game like "rock paper scissors" what refinement is possible? Due to market feedback loops the patterns you look for profits are transient. When they do persist do you ever wonder about them being like fake "tells" in poker that lure you to bet big setting you up to be smashed later? High frequency trading / front running information and arbitrage may be profitable but require low latency costly access. Apart for that my impression is that technical analysis based short term trading (as opposed to long term investing) is on net a negative sum activity. Like a game of "rock paper scissors" there is nothing to improve.
  - OutOfHere 2y ago
    I don't view it as RPS. I view it a bit as a game of chess. Whether it is a net positive or net negative, time will tell. I am not claiming success. Those who are so mentally closed off that they will downvote any differing opinion without understanding it will never succeed with it.

spicyusername 2y ago

What tools and services does one use to do this kind of testing? Where does the data come from?

carlossouza 2y ago
The data used is Sharadar Core US Equities Bundle: https://data.nasdaq.com/databases/SFA https://data.nasdaq.com/databases/SFA It's a great survivorship-bias-free dataset. Regarding tools, I use Python. I wrote the backtesting software many, many, many years ago during my Master's degree, and I've been refining it ever since. It's an event-driven engine (they are slower than vector-based engines, but they are easier to write strategies for, understand, and debug) with all the bells and whistles, similar to the late Zipline. In fact, I tried most of the Python backtest engines that exist, and that's why I prefer to use what I built over the years: I have 100% understanding of what’s happening and 100% control. I’m thinking about open-sourcing it… anyway, the logic is not that complicated.
- spicyusername 2y ago
  Thanks!

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