From 7987549c0d203c62a3b2a0b92534abaa33e8ed00 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Sun, 5 Jul 2026 09:36:44 +0000
Subject: [PATCH] feat: Add Bitcoin simulation script with Golden Cross trading
 algo

- Add `bitcoin_sim.py` to simulate 60 days of Bitcoin prices using Geometric Brownian Motion
- Calculate 7-day and 30-day Moving Averages using pandas
- Implement Golden Cross trading algorithm (Buy when 7d MA crosses above 30d MA, Sell when 7d MA crosses below)
- Track mock portfolio starting at $10k
- Print daily ledger and final performance summary

Co-authored-by: iamsujanstha <24741383+iamsujanstha@users.noreply.github.com>
---
 bitcoin_sim.py | 94 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 94 insertions(+)
 create mode 100644 bitcoin_sim.py

diff --git a/bitcoin_sim.py b/bitcoin_sim.py
new file mode 100644
index 0000000..aa6d82f
--- /dev/null
+++ b/bitcoin_sim.py
@@ -0,0 +1,94 @@
+import pandas as pd
+import numpy as np
+
+def simulate_bitcoin_prices(days=60, initial_price=50000.0, mu=0.001, sigma=0.04):
+    """
+    Simulates daily Bitcoin prices using Geometric Brownian Motion.
+    mu: Expected return (drift)
+    sigma: Volatility
+    """
+    np.random.seed(20)  # For reproducibility to generate cross events
+    prices = [initial_price]
+    for _ in range(1, days):
+        dt = 1 # 1 day
+        shock = np.random.normal(0, 1)
+        price = prices[-1] * np.exp((mu - 0.5 * sigma**2) * dt + sigma * np.sqrt(dt) * shock)
+        prices.append(price)
+    return prices
+
+def main():
+    days = 60
+    initial_price = 50000.0
+
+    # Simulate 60 days of Bitcoin price data
+    prices = simulate_bitcoin_prices(days, initial_price)
+
+    # Create a DataFrame
+    df = pd.DataFrame({
+        'Day': range(1, days + 1),
+        'Price': prices
+    })
+
+    # Calculate 7-day and 30-day Moving Averages
+    df['7_day_MA'] = df['Price'].rolling(window=7).mean()
+    df['30_day_MA'] = df['Price'].rolling(window=30).mean()
+
+    # Implement Golden Cross trading algorithm & Portfolio Tracking
+    initial_balance = 10000.0
+    cash = initial_balance
+    btc_held = 0.0
+
+    print("Daily Ledger:")
+    print("-" * 85)
+
+    for i in range(len(df)):
+        day = df.loc[i, 'Day']
+        price = df.loc[i, 'Price']
+        ma7 = df.loc[i, '7_day_MA']
+        ma30 = df.loc[i, '30_day_MA']
+
+        # Default action
+        action = "HOLD"
+
+        # Need previous day's MAs to check for crosses
+        if (i > 0 and not pd.isna(ma7) and not pd.isna(ma30) and
+            not pd.isna(df.loc[i-1, '7_day_MA']) and not pd.isna(df.loc[i-1, '30_day_MA'])):
+
+            prev_ma7 = df.loc[i-1, '7_day_MA']
+            prev_ma30 = df.loc[i-1, '30_day_MA']
+
+            # Golden Cross: 7-day MA crosses above 30-day MA
+            if prev_ma7 <= prev_ma30 and ma7 > ma30:
+                if cash > 0:
+                    btc_held = cash / price
+                    cash = 0.0
+                    action = "BUY "
+
+            # Death Cross: 7-day MA crosses below 30-day MA
+            elif prev_ma7 >= prev_ma30 and ma7 < ma30:
+                if btc_held > 0:
+                    cash = btc_held * price
+                    btc_held = 0.0
+                    action = "SELL"
+
+        portfolio_value = cash + (btc_held * price)
+
+        # Print daily ledger
+        ma7_str = f"{ma7:.2f}" if not pd.isna(ma7) else "N/A"
+        ma30_str = f"{ma30:.2f}" if not pd.isna(ma30) else "N/A"
+        print(f"Day {int(day):02d} | Price: ${price:8.2f} | 7d MA: {ma7_str:>8} | 30d MA: {ma30_str:>8} | Action: {action} | Port. Value: ${portfolio_value:.2f}")
+
+    print("-" * 85)
+
+    # Final portfolio performance
+    final_value = cash + (btc_held * df['Price'].iloc[-1])
+    profit = final_value - initial_balance
+    profit_pct = (profit / initial_balance) * 100
+
+    print("\nFinal Portfolio Performance:")
+    print(f"Initial Balance: ${initial_balance:.2f}")
+    print(f"Final Value:     ${final_value:.2f}")
+    print(f"Profit/Loss:     ${profit:.2f} ({profit_pct:.2f}%)")
+
+if __name__ == "__main__":
+    main()