🎨 Palette: Add CLI arguments for configuration and accessibility

.Jules/palette.md

@@ -1,3 +1,7 @@
 ## 2024-05-22 - Visual Hierarchy in CLI Output
 **Learning:** Adding color-coded indicators (Green/Red) and emojis (💰, 📉) in CLI tools significantly reduces cognitive load when parsing financial data streams. It transforms a wall of text into a scannable narrative.
 **Action:** For data-heavy CLI applications, always implement a semantic color system and visual anchors (icons/emojis) for key events.
+
+## 2024-05-23 - CLI Accessibility and Configuration
+**Learning:** While color-coding is great, it must be optional. Adding a `--no-color` flag is crucial for accessibility (e.g., color blindness, strict terminal environments). Similarly, a `--quiet` flag respects the user's need for concise output in automated pipelines.
+**Action:** Always include `--no-color` and `--quiet` flags in CLI tools to support diverse user needs and environments.

.gitignore

@@ -39,3 +39,7 @@
 
 # debug information files
 *.dwo
+
+# Python
+__pycache__/
+*.pyc

bitcoin_trading_simulation.py

@@ -1,3 +1,4 @@
+import argparse
 import numpy as np
 import pandas as pd
 
@@ -9,6 +10,15 @@ class Colors:
     ENDC = '\033[0m'
     BOLD = '\033[1m'
 
+    @classmethod
+    def disable(cls):
+        cls.HEADER = ''
+        cls.BLUE = ''
+        cls.GREEN = ''
+        cls.RED = ''
+        cls.ENDC = ''
+        cls.BOLD = ''
+
 def simulate_bitcoin_prices(days=60, initial_price=50000, volatility=0.02):
     """
     Simulates Bitcoin prices for a given number of days using Geometric Brownian Motion.
@@ -49,7 +59,7 @@ def generate_trading_signals(signals):
     signals['positions'] = signals['signal'].diff().shift(1)
     return signals
 
-def simulate_trading(signals, initial_cash=10000):
+def simulate_trading(signals, initial_cash=10000, quiet=False):
     """
     Simulates trading based on signals and prints a daily ledger.
     """
@@ -59,7 +69,8 @@ def simulate_trading(signals, initial_cash=10000):
     portfolio['btc'] = 0.0
     portfolio['total_value'] = float(initial_cash)
 
-    print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
+    if not quiet:
+        print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
     for i, row in signals.iterrows():
         if i > 0:
             portfolio.loc[i, 'cash'] = portfolio.loc[i-1, 'cash']
@@ -70,24 +81,40 @@ def simulate_trading(signals, initial_cash=10000):
             btc_to_buy = portfolio.loc[i, 'cash'] / row['price']
             portfolio.loc[i, 'btc'] += btc_to_buy
             portfolio.loc[i, 'cash'] -= btc_to_buy * row['price']
-            print(f"{Colors.GREEN}Day {i}: 💰 Buy {btc_to_buy:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
+            if not quiet:
+                print(f"{Colors.GREEN}Day {i}: 💰 Buy {btc_to_buy:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
 
         # Sell signal
         elif row['positions'] == -2.0:
             if portfolio.loc[i, 'btc'] > 0:
                 cash_received = portfolio.loc[i, 'btc'] * row['price']
                 portfolio.loc[i, 'cash'] += cash_received
-                print(f"{Colors.RED}Day {i}: 📉 Sell {portfolio.loc[i, 'btc']:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
+                if not quiet:
+                    print(f"{Colors.RED}Day {i}: 📉 Sell {portfolio.loc[i, 'btc']:.4f} BTC at ${row['price']:.2f}{Colors.ENDC}")
                 portfolio.loc[i, 'btc'] = 0
 
         portfolio.loc[i, 'total_value'] = portfolio.loc[i, 'cash'] + portfolio.loc[i, 'btc'] * row['price']
-        print(f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
+        if not quiet:
+            print(f"Day {i}: Portfolio Value: ${portfolio.loc[i, 'total_value']:.2f}, Cash: ${portfolio.loc[i, 'cash']:.2f}, BTC: {portfolio.loc[i, 'btc']:.4f}")
 
     return portfolio
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Bitcoin Trading Simulation")
+    parser.add_argument("--days", type=int, default=60, help="Number of days to simulate (default: 60)")
+    parser.add_argument("--initial-cash", type=float, default=10000, help="Initial cash amount (default: 10000)")
+    parser.add_argument("--initial-price", type=float, default=50000, help="Initial Bitcoin price (default: 50000)")
+    parser.add_argument("--volatility", type=float, default=0.02, help="Volatility factor (default: 0.02)")
+    parser.add_argument("--quiet", action="store_true", help="Suppress daily output")
+    parser.add_argument("--no-color", action="store_true", help="Disable color output")
+
+    args = parser.parse_args()
+
+    if args.no_color:
+        Colors.disable()
+
     # Simulate prices
-    prices = simulate_bitcoin_prices()
+    prices = simulate_bitcoin_prices(days=args.days, initial_price=args.initial_price, volatility=args.volatility)
 
     # Calculate moving averages
     signals = calculate_moving_averages(prices)
@@ -96,11 +123,11 @@ def simulate_trading(signals, initial_cash=10000):
     signals = generate_trading_signals(signals)
 
     # Simulate trading
-    portfolio = simulate_trading(signals)
+    portfolio = simulate_trading(signals, initial_cash=args.initial_cash, quiet=args.quiet)
 
     # Final portfolio performance
     final_value = portfolio['total_value'].iloc[-1]
-    initial_cash = 10000
+    initial_cash = args.initial_cash
     profit = final_value - initial_cash
 
     # Compare with buy and hold strategy

test_bitcoin_trading.py

@@ -0,0 +1,69 @@
+import unittest
+import pandas as pd
+import numpy as np
+from bitcoin_trading_simulation import (
+    simulate_bitcoin_prices,
+    calculate_moving_averages,
+    generate_trading_signals,
+    Colors
+)
+
+class TestBitcoinTrading(unittest.TestCase):
+    def test_simulate_bitcoin_prices(self):
+        days = 10
+        initial_price = 100
+        prices = simulate_bitcoin_prices(days=days, initial_price=initial_price)
+        self.assertIsInstance(prices, pd.Series)
+        self.assertEqual(len(prices), days)
+        self.assertEqual(prices.iloc[0], initial_price)
+
+    def test_calculate_moving_averages(self):
+        prices = pd.Series([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], name='Price')
+        signals = calculate_moving_averages(prices, short_window=2, long_window=5)
+        self.assertIn('short_mavg', signals.columns)
+        self.assertIn('long_mavg', signals.columns)
+        # Check values: window=2, min_periods=1
+        # Day 0: 10
+        # Day 1: (10+11)/2 = 10.5
+        self.assertEqual(signals['short_mavg'].iloc[1], 10.5)
+
+    def test_generate_trading_signals(self):
+        # Create a dummy signals DataFrame
+        data = {
+            'price': [100, 101, 102, 103],
+            'short_mavg': [100, 105, 90, 95],
+            'long_mavg':  [100, 100, 100, 100]
+        }
+        signals = pd.DataFrame(data)
+        signals = generate_trading_signals(signals)
+
+        self.assertIn('signal', signals.columns)
+        self.assertIn('positions', signals.columns)
+
+        # Day 1: short (105) > long (100) -> signal 1.0 (Buy)
+        self.assertEqual(signals['signal'].iloc[1], 1.0)
+
+        # Day 2: short (90) < long (100) -> signal -1.0 (Sell)
+        self.assertEqual(signals['signal'].iloc[2], -1.0)
+
+        # Positions are shifted diff
+        # signal: 0, 1, -1, -1
+        # diff: NaN, 1, -2, 0
+        # shifted: NaN, NaN, 1, -2
+        self.assertTrue(np.isnan(signals['positions'].iloc[0]))
+        self.assertTrue(np.isnan(signals['positions'].iloc[1]))
+        self.assertEqual(signals['positions'].iloc[2], 1.0)
+        self.assertEqual(signals['positions'].iloc[3], -2.0)
+
+    def test_colors_disable(self):
+        # Save original values just in case
+        original_header = Colors.HEADER
+
+        Colors.disable()
+        self.assertEqual(Colors.HEADER, '')
+        self.assertEqual(Colors.GREEN, '')
+        self.assertEqual(Colors.RED, '')
+        self.assertEqual(Colors.ENDC, '')
+
+if __name__ == '__main__':
+    unittest.main()