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.
I haven't read Taleb's book. But wouldn't the logic you employ apply to any strategy?
eg I could also say that you could fool yourself believing that investing in the S&P 500 index is a strategy with high-probability of performing well, with a hidden surprise of low-prob cat losses.
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.
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.
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.
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.
czl|1 year ago
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.
mmarian|1 year ago
eg I could also say that you could fool yourself believing that investing in the S&P 500 index is a strategy with high-probability of performing well, with a hidden surprise of low-prob cat losses.
OutOfHere|1 year ago
OutOfHere|1 year ago
dbs|1 year ago
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|1 year ago
spicyusername|1 year ago
Where does the data come from?
carlossouza|1 year ago
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.
unknown|1 year ago
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unknown|1 year ago
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