I Ran a 4-Strategy AI Trading Tournament in Paper Trading — Here's Who Won

The Tournament Idea

Every algo trader has opinions about which strategy is better. RSI mean reversion? MACD crossover? Momentum? Everyone argues. Nobody runs the experiment.

So I set up a controlled paper trading tournament inside TradeSight: four strategies, same universe of stocks, same starting capital ($500 each), running in parallel for 30 days. No cherry-picking. No curve-fitting. Just run them and see.

Here's what happened.

The Competitors

Strategy 1: RSI Mean Reversion
Buy when RSI < 30 (oversold). Sell when RSI > 70 (overbought) or after 5 days.
Classic momentum fade. Works in range-bound markets. Gets destroyed in trends.

Strategy 2: MACD Crossover
Buy when MACD line crosses above signal line. Sell on reverse cross or 10% trailing stop.
Trend-following. Slower to enter, slower to exit. Less noise, more whipsaw.

Strategy 3: Bollinger Band Squeeze
Buy when price crosses above lower band after a squeeze (bands tighten). Exit on upper band touch.
Volatility-based. Works well after consolidation periods.

Strategy 4: AI Confluence (my original)
Requires 2 of 3 signals aligned: RSI < 35, MACD bullish, price above 20-day SMA. Exits on any signal flipping.
More selective. Should have higher win rate, lower trade count.

Setting Up the Tournament

The core is a shared paper trading engine with isolated strategy contexts:

from tradesight.paper_trader import PaperTrader
from tradesight.strategies import RSIMeanReversion, MACDCrossover, BollingerSqueeze, AIConfluence

UNIVERSE = ["AAPL", "GOOG", "MSFT", "AMZN", "TSLA", "NVDA", "META", "V", "JPM", "ADBE"]
STARTING_CAPITAL = 500.0

tournament = {
    "rsi": PaperTrader(RSIMeanReversion(), capital=STARTING_CAPITAL),
    "macd": PaperTrader(MACDCrossover(), capital=STARTING_CAPITAL),
    "bollinger": PaperTrader(BollingerSqueeze(), capital=STARTING_CAPITAL),
    "confluence": PaperTrader(AIConfluence(), capital=STARTING_CAPITAL),
}

# Each market day, run signals for each strategy
for symbol in UNIVERSE:
    data = fetch_daily(symbol)
    for name, trader in tournament.items():
        trader.evaluate(symbol, data)

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One critical rule: no look-ahead bias. Each strategy only sees data up to the current simulated day. This is the most common mistake in backtesting — and why most backtests look better than live trading.

The Results (30 Days)

Strategy

Trades

Win Rate

Total P&L

Final Value

RSI Mean Reversion

9

33%

-$146

$354

MACD Crossover

4

50%

-$32

$468

Bollinger Squeeze

6

50%

+$14

$514

AI Confluence

3

67%

+$28

$528

Winner: AI Confluence — but barely, and with only 3 trades.

What the Tournament Taught Me

1. Trade count matters. RSI took 9 trades, lost on 6 of them, and compounded the losses. MACD's lower trade count reduced drawdown exposure.

2. Win rate without position sizing is meaningless. A 67% win rate with 3 trades is statistically weak. I need 20+ trades before trusting a win rate.

3. Market regime kills everything. March 2026 was a high-volatility trending month. RSI mean reversion was built for range-bound conditions. It got crushed.

4. The "best" strategy changes every month. Bollinger Squeeze was flat or negative in trending months. It would dominate in a consolidating market.

The Honest Takeaway

Nobody wins a trading tournament over 30 days. That's not enough time. But the tournament structure itself is valuable — it forces you to run strategies in parallel without retrofitting results.

The goal isn't to find the "best" strategy. It's to understand when each strategy works and build a rotation system around market regimes.

That's what I'm building into TradeSight next: a regime detector that routes signals to the right strategy based on current volatility and trend strength. Tournament results become training data for the router.

→ TradeSight on GitHub — open source algo trading framework with paper trading, backtesting, and strategy tournaments built in.

Source: Dev.to