Markets
SPY5,248.32+0.35%
QQQ18,432.10+0.52%
BTC98,420+1.27%
ETH3,812-1.10%
NVDA952.30+1.36%
AAPL178.42-1.19%
TSLA168.90+2.62%
EUR/USD1.0892-0.21%
GC2,342.50+0.36%
CL82.15+1.17%
SOL178.50+3.36%
DXY104.82+0.14%
SPY5,248.32+0.35%
QQQ18,432.10+0.52%
BTC98,420+1.27%
ETH3,812-1.10%
NVDA952.30+1.36%
AAPL178.42-1.19%
TSLA168.90+2.62%
EUR/USD1.0892-0.21%
GC2,342.50+0.36%
CL82.15+1.17%
SOL178.50+3.36%
DXY104.82+0.14%
TF
tf/algorithms
534
tf/algorithms•Posted by u/algo_trader_42••1 min read

Why most backtests are useless — the 7 biases you need to know

After 3 years of algo trading and hundreds of backtests, here's why 90% of backtests you see online are misleading:

  1. Survivorship bias: you test on stocks that still exist. The ones that went bankrupt are excluded. Inflates returns.
  2. Look-ahead bias: using data that wasn't available at the time of the trade. Even split adjustment can cause this.
  3. Overfitting: 20 parameters optimized over 10 years = perfect curve, catastrophic real performance.
  4. Slippage ignored: "I made 200%" — with perfect fills at mid-price that you'll never get in reality.
  5. Transaction costs: commission + spread + market impact. On a high-frequency algo, it changes everything.
  6. Data snooping: testing 50 strategies and keeping only the best one. Probability it works forward: close to zero.
  7. Regime change: what worked in 2015-2020 (infinite QE, zero rates) doesn't work in 2024-2026 (high rates, inflation).
5 Comments

Connectez-vous pour laisser un commentaire

Sort by:
macro_man·edited

#6 (data snooping) is the most insidious because people don't even realize they're doing it. You read an article, test the strategy on the same data as the author, see it works, and think you're smart. But the information leaked.

112
chart_wizard·edited

Solution for #3: use an out-of-sample approach. Train on 2015-2020, test on 2021-2023, validate on 2024-2026. If it survives all 3 periods, it might be real.

89
algo_trader_42OP·edited

Yes but even that has limits. With enough combinations, you always find something that works across 3 periods by chance. Walk-forward is better but requires years of data.

56
newbie_investor·edited

As a beginner in algo trading, this is depressing to read. Are there any simple strategies that actually work despite all these biases?

67
algo_trader_42OP·edited

Simple trend following (be long when price is above the 200 MA, cash below) survives almost every robustness test over 100 years. It's not sexy but it works. The key is simplicity — fewer parameters = less overfitting.

78