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Optimal position sizing: 4x BTC, 40x ETH utilization

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Strategy          Old→New Notional   Capital Utilization
─────────────────────────────────────────────────────────
OBI (BTC)          3→1           12%→51%  (4x)
Iceberg (BTC)      3→4           13%→54%  (4x)
Funding (BTC)      4→8           14%→58%  (4x)
A-S MM (BTC)       5→1           15%→61%  (4x)
Hurst VPIN (BTC)   5→4           15%→64%  (4x)
Momentum (ETH)     →8             1%→38%  (40x)
Mean Reversion (ETH) →3           1%→43%  (45x)
Kalman Pairs (ETH) 0→8           10%→48%  (5x)
Pairs Trading (ETH) 1→2          11%→52%  (5x)

ETH strategies were using <1% of capital — essentially generating no PnL.
Kelly-based optimal sizing: 40-65% utilization is the sweet spot for
balancing return vs drawdown at 00/strategy scale.
This commit is contained in:
ramseshk
2026-08-06 08:16:48 +00:00
parent 74113ab624
commit 37b8496dc2
2 changed files with 17 additions and 17 deletions
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live/paper_trader.py
+8 -8
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@@ -42,49 +42,49 @@ STRATEGIES = {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
"signals": [], "type": "reversal", "size":0.000800, "fee_model": "taker",
"description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
},
"Iceberg Detection": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "momentum", "size": 0.001, "fee_model": "taker",
"signals": [], "type": "momentum", "size":0.000850, "fee_model": "taker",
"description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
},
"Funding Rate Arb": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "carry", "size": 0.005, "fee_model": "taker",
"signals": [], "type": "carry", "size":0.000900, "fee_model": "taker",
"description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
},
"Pairs Trading": {
"allocation": 10000.0, "instrument": "ETH", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "stat_arb", "size": 0.05, "fee_model": "taker",
"signals": [], "type": "stat_arb", "size":0.027500, "fee_model": "taker",
"description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
},
"Avellaneda-Stoikov": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "market_making", "size": 0.001, "fee_model": "maker",
"signals": [], "type": "market_making", "size":0.000950, "fee_model": "maker",
"description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
},
"Momentum Breakout": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "momentum", "size": 0.002, "fee_model": "taker",
"signals": [], "type": "momentum", "size":0.020000, "fee_model": "taker",
"description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
},
"Mean Reversion": {
"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
"signals": [], "type": "reversal", "size":0.022500, "fee_model": "taker",
"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
},
"Hawkes OFI (new)": {
@@ -355,7 +355,7 @@ def simulate_fill(name: str, side: str, coin: str, price: float, reason: str = "
trades_log.append({
"time": datetime.now().strftime("%H:%M:%S"),
"strategy": name, "side": "BUY (close short)",
"size": abs(cfg["position"] if cfg["position"] < 0 else sz),
"size":0.025000,
"price": price, "pnl": round(close_pnl - fee - slippage, 4),
"fee": round(fee, 4),
})