🎨 Palette: Enhanced CLI UX and fixed accessibility crash

.Jules/palette.md

@@ -1,3 +1,7 @@
 ## 2024-05-23 - CLI UX Enhancement
 **Learning:** Even in CLI apps, visual distinction (colors, emojis) significantly reduces cognitive load when scanning logs.
 **Action:** Use ANSI colors and consistent emojis for key events (success/failure) in future CLI tools.
+
+## 2025-05-27 - CLI Accessibility & Robustness
+**Learning:** Hardcoded ANSI colors without a disable mechanism exclude users with accessibility needs or those parsing logs. A crash when disabling colors is a critical failure.
+**Action:** Always implement a robust `--no-color` flag and ensure `Colors` class handles disabling correctly. Input validation preventing runtime crashes (like ZeroDivisionError or negative inputs) is critical UX.

bitcoin_trading_simulation.py

@@ -1,38 +1,35 @@
 import argparse
 import numpy as np
 import pandas as pd
-import argparse
+import sys
 
 
 class Colors:
     HEADER = '\033[95m'
     BLUE = '\033[94m'
+    CYAN = '\033[96m'
     GREEN = '\033[92m'
-    RED = '\033[91m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
+    RED = '\033[91m'  # Alias for FAIL/RED compatibility
     ENDC = '\033[0m'
     BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
 
     @classmethod
     def disable(cls):
         cls.HEADER = ''
         cls.BLUE = ''
+        cls.CYAN = ''
         cls.GREEN = ''
+        cls.WARNING = ''
+        cls.FAIL = ''
         cls.RED = ''
         cls.ENDC = ''
         cls.BOLD = ''
+        cls.UNDERLINE = ''
 
 
-class Colors:
-    HEADER = '\033[95m'
-    BLUE = '\033[94m'
-    CYAN = '\033[96m'
-    GREEN = '\033[92m'
-    WARNING = '\033[93m'
-    FAIL = '\033[91m'
-    ENDC = '\033[0m'
-    BOLD = '\033[1m'
-    UNDERLINE = '\033[4m'
-
 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.
@@ -87,9 +84,8 @@ def simulate_trading(signals, initial_cash=10000, quiet=False):
     portfolio['total_value'] = float(initial_cash)
 
     if not quiet:
-        print(f"{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
+        print(f"\n{Colors.HEADER}{Colors.BOLD}------ Daily Trading Ledger ------{Colors.ENDC}")
 
-    print(f"\n{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']
@@ -133,6 +129,20 @@ def simulate_trading(signals, initial_cash=10000, quiet=False):
     if args.no_color:
         Colors.disable()
 
+    # Input Validation
+    if args.days <= 0:
+        print(f"{Colors.FAIL}Error: Days must be a positive integer.{Colors.ENDC}")
+        sys.exit(1)
+    if args.initial_cash <= 0:
+        print(f"{Colors.FAIL}Error: Initial cash must be positive.{Colors.ENDC}")
+        sys.exit(1)
+    if args.initial_price <= 0:
+        print(f"{Colors.FAIL}Error: Initial price must be positive.{Colors.ENDC}")
+        sys.exit(1)
+    if args.volatility < 0:
+        print(f"{Colors.FAIL}Error: Volatility cannot be negative.{Colors.ENDC}")
+        sys.exit(1)
+
     # Simulate prices
     prices = simulate_bitcoin_prices(days=args.days, initial_price=args.initial_price, volatility=args.volatility)
 
@@ -149,17 +159,30 @@ def simulate_trading(signals, initial_cash=10000, quiet=False):
     final_value = portfolio['total_value'].iloc[-1]
     initial_cash = args.initial_cash
     profit = final_value - initial_cash
+    roi = (profit / initial_cash) * 100
+
+    # Trade Statistics
+    # Calculate total trades by tracking changes in BTC holdings.
+    btc_diff = portfolio['btc'].diff().fillna(0)
+    buys = len(btc_diff[btc_diff > 0])
+    sells = len(btc_diff[btc_diff < 0])
+    total_trades = buys + sells
 
     # Compare with buy and hold strategy
     buy_and_hold_btc = args.initial_cash / prices.iloc[0]
     buy_and_hold_value = buy_and_hold_btc * prices.iloc[-1]
-
-    print(f"\n{Colors.HEADER}{Colors.BOLD}------ Final Portfolio Performance ------{Colors.ENDC}")
-    print(f"Initial Cash: ${initial_cash:.2f}")
-    print(f"Final Portfolio Value: ${final_value:.2f}")
+
+    print(f"\n{Colors.HEADER}╔════════════════════════════════════════╗{Colors.ENDC}")
+    print(f"{Colors.HEADER}║      Final Portfolio Performance       ║{Colors.ENDC}")
+    print(f"{Colors.HEADER}╚════════════════════════════════════════╝{Colors.ENDC}")
+    print(f"Initial Cash:            ${initial_cash:,.2f}")
+    print(f"Final Portfolio Value:   ${final_value:,.2f}")
     if profit >= 0:
-        print(f"{Colors.GREEN}💰 Profit/Loss: ${profit:.2f}{Colors.ENDC}")
+        print(f"{Colors.GREEN}💰 Profit/Loss:          ${profit:,.2f} ({roi:+.2f}%){Colors.ENDC}")
     else:
-        print(f"{Colors.FAIL}📉 Profit/Loss: ${profit:.2f}{Colors.ENDC}")
-    print(f"Buy and Hold Strategy Value: ${buy_and_hold_value:.2f}")
-    print(f"{Colors.HEADER}-----------------------------------------{Colors.ENDC}")
+        print(f"{Colors.FAIL}📉 Profit/Loss:          ${profit:,.2f} ({roi:+.2f}%){Colors.ENDC}")
+
+    print(f"{Colors.CYAN}------------------------------------------{Colors.ENDC}")
+    print(f"Total Trades:            {total_trades} (Buys: {buys}, Sells: {sells})")
+    print(f"Buy and Hold Value:      ${buy_and_hold_value:,.2f}")
+    print(f"{Colors.CYAN}------------------------------------------{Colors.ENDC}")

test_simulation.py

@@ -1,22 +1,24 @@
-import pytest
 import pandas as pd
 import numpy as np
 from bitcoin_trading_simulation import simulate_bitcoin_prices, calculate_moving_averages, generate_trading_signals
 
+
 def test_simulate_bitcoin_prices():
     days = 10
     prices = simulate_bitcoin_prices(days=days, initial_price=50000)
     assert len(prices) == days
     assert isinstance(prices, pd.Series)
     assert prices.name == 'Price'
 
+
 def test_calculate_moving_averages():
     prices = pd.Series([100, 101, 102, 103, 104, 105, 106, 107, 108, 109], name='Price')
     signals = calculate_moving_averages(prices, short_window=3, long_window=5)
     assert 'short_mavg' in signals.columns
     assert 'long_mavg' in signals.columns
     assert not signals['short_mavg'].isnull().all()
 
+
 def test_generate_trading_signals():
     # Create dummy signals DataFrame
     data = {