Position Sizing
Methods

import math
import pandas as pd

class PositionSizer:
    def __init__(self, lot_size: int = 50, max_lots: int = 10):
        self.lot_size = lot_size
        self.max_lots = max_lots

    # ── Method 1: Fixed lots ──────────────────────────────────
    def fixed_lots(self, n: int = 1) -> int:
        return min(n, self.max_lots)

    # ── Method 2: Fixed % risk ────────────────────────────────
    def fixed_pct_risk(
        self,
        equity      : float,
        entry_price : float,
        stop_price  : float,
        risk_pct    : float = 1.0,  # risk 1% of equity per trade
    ) -> int:
        """
        lots = (equity × risk_pct%) / (|entry - stop| × lot_size)
        """
        risk_per_lot = abs(entry_price - stop_price) * self.lot_size
        if risk_per_lot == 0:
            return 1
        risk_rs = equity * (risk_pct / 100)
        lots    = int(risk_rs / risk_per_lot)
        return max(1, min(lots, self.max_lots))

    # ── Method 3: ATR-based sizing ────────────────────────────
    def atr_based(
        self,
        equity       : float,
        atr          : float,
        risk_pct     : float = 1.0,
        atr_multiplier: float = 1.5,  # stop = entry ± 1.5×ATR
    ) -> int:
        stop_distance = atr * atr_multiplier
        risk_rs   = equity * (risk_pct / 100)
        lots      = int(risk_rs / (stop_distance * self.lot_size))
        return max(1, min(lots, self.max_lots))

    # ── Method 4: Kelly Criterion ─────────────────────────────
    def kelly(
        self,
        equity       : float,
        win_rate     : float,  # 0.0–1.0
        avg_win      : float,  # in points
        avg_loss     : float,  # in points (positive number)
        fraction     : float = 0.5,  # half-Kelly for safety
    ) -> int:
        """
        Kelly fraction = W/L - (1-W)/G
        where W = win rate, L = avg loss, G = avg win
        """
        if avg_loss == 0 or win_rate <= 0:
            return 1
        payoff_ratio = avg_win / avg_loss
        kelly_f = win_rate - ((1 - win_rate) / payoff_ratio)
        kelly_f = max(0, kelly_f) * fraction  # half-Kelly

        # Convert fraction to lots based on account size
        capital_to_risk = equity * kelly_f
        lots = int(capital_to_risk / (avg_loss * self.lot_size))
        return max(1, min(lots, self.max_lots))

# Strategy stats from last 100 trades:
win_rate = 0.55       # 55% win rate
avg_win  = 80         # 80 pts avg winner
avg_loss = 50         # 50 pts avg loser

payoff   = avg_win / avg_loss            # = 1.6
kelly_f  = win_rate - (1 - win_rate) / payoff
# kelly_f = 0.55 - 0.45/1.6 = 0.55 - 0.28 = 0.27 (27%)

half_kelly = kelly_f / 2               # = 0.135 (13.5% of equity)

equity = 500_000
capital_at_risk = equity * half_kelly   # = ₹67,500
lots = int(capital_at_risk / (avg_loss * 50))  # 50=lot_size
print(f"Kelly lots: {lots}")             # = 27 lots