🎨 Palette: Enhance CLI Report with ASCII Dashboard & Stats

.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.
+
+## 2025-02-28 - Structured CLI Reports
+**Learning:** Dense numerical data in CLI output is hard to parse. Using ASCII box-drawing characters and alignment to create a "dashboard" or "invoice" style summary significantly improves readability and perceived quality.
+**Action:** When summarizing simulation or batch job results, always format the final report as a structured table or box rather than a list of print statements.

bitcoin.py

@@ -0,0 +1,24 @@
+import requests
+
+
+def get_bitcoin_price():
+    """
+    Fetches the current Bitcoin price from CoinDesk API.
+    """
+    url = "https://api.coindesk.com/v1/bpi/currentprice.json"
+    try:
+        response = requests.get(url)
+        if response.status_code == 200:
+            data = response.json()
+            return data["bpi"]["USD"]["rate_float"]
+        else:
+            raise ConnectionError(f"API returned status code {response.status_code}")
+    except requests.RequestException:
+        raise ConnectionError("Failed to fetch Bitcoin price")
+
+
+def calculate_value(amount, price):
+    """
+    Calculates the total value of Bitcoin based on the amount and current price.
+    """
+    return float(amount) * float(price)

bitcoin_trading_simulation.py

@@ -1,38 +1,32 @@
 import argparse
 import numpy as np
 import pandas as pd
-import argparse
 
 
 class Colors:
     HEADER = '\033[95m'
     BLUE = '\033[94m'
+    CYAN = '\033[96m'
     GREEN = '\033[92m'
-    RED = '\033[91m'
+    WARNING = '\033[93m'
+    FAIL = '\033[91m'
     ENDC = '\033[0m'
     BOLD = '\033[1m'
+    UNDERLINE = '\033[4m'
 
     @classmethod
     def disable(cls):
         cls.HEADER = ''
         cls.BLUE = ''
+        cls.CYAN = ''
         cls.GREEN = ''
-        cls.RED = ''
+        cls.WARNING = ''
+        cls.FAIL = ''
         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 +81,7 @@ 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']
@@ -153,13 +145,51 @@ def simulate_trading(signals, initial_cash=10000, quiet=False):
     # 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}")
-    if profit >= 0:
-        print(f"{Colors.GREEN}💰 Profit/Loss: ${profit:.2f}{Colors.ENDC}")
-    else:
-        print(f"{Colors.FAIL}📉 Profit/Loss: ${profit:.2f}{Colors.ENDC}")
-    print(f"Buy and Hold Strategy Value: ${buy_and_hold_value:.2f}")
-    print(f"{Colors.HEADER}-----------------------------------------{Colors.ENDC}")
+
+    # Calculate additional statistics
+    roi = (profit / initial_cash) * 100
+    trade_count_buys = int(portfolio['btc'].diff().fillna(0).gt(0).sum())
+    trade_count_sells = int(portfolio['btc'].diff().fillna(0).lt(0).sum())
+    total_trades = trade_count_buys + trade_count_sells
+    vs_buy_hold = final_value - buy_and_hold_value
+
+    # Format the final report
+    width = 44
+    border = "═" * width
+
+    print(f"\n{Colors.HEADER}{Colors.BOLD}╔{border}╗{Colors.ENDC}")
+    title = "Final Portfolio Performance"
+    print(f"{Colors.HEADER}{Colors.BOLD}║{title:^{width}}║{Colors.ENDC}")
+    print(f"{Colors.HEADER}{Colors.BOLD}╠{border}╣{Colors.ENDC}")
+
+    def print_line(label, value_str, color=Colors.ENDC):
+        left_border = f"{Colors.HEADER}{Colors.BOLD}║{Colors.ENDC}"
+        right_border = f"{Colors.HEADER}{Colors.BOLD}║{Colors.ENDC}"
+        print(f"{left_border} {label:<24}{color}{value_str:>18}{Colors.ENDC} {right_border}")
+
+    print_line("Initial Cash:", f"${initial_cash:,.2f}")
+    print_line("Final Portfolio Value:", f"${final_value:,.2f}")
+
+    profit_color = Colors.GREEN if profit >= 0 else Colors.FAIL
+    profit_sign = "+" if profit >= 0 else "-"
+    print_line("Profit/Loss:", f"{profit_sign}${abs(profit):,.2f}", profit_color)
+
+    roi_color = Colors.GREEN if roi >= 0 else Colors.FAIL
+    roi_sign = "+" if roi >= 0 else "-"
+    print_line("ROI:", f"{roi_sign}{abs(roi):.2f}%", roi_color)
+
+    print(f"{Colors.HEADER}{Colors.BOLD}╠{border}╣{Colors.ENDC}")
+
+    print_line("Total Trades:", f"{total_trades}")
+    print_line("  - Buys:", f"{trade_count_buys}")
+    print_line("  - Sells:", f"{trade_count_sells}")
+
+    print(f"{Colors.HEADER}{Colors.BOLD}╠{border}╣{Colors.ENDC}")
+
+    print_line("Buy & Hold Value:", f"${buy_and_hold_value:,.2f}")
+
+    vs_color = Colors.GREEN if vs_buy_hold >= 0 else Colors.FAIL
+    vs_sign = "+" if vs_buy_hold >= 0 else "-"
+    print_line("vs Buy & Hold:", f"{vs_sign}${abs(vs_buy_hold):,.2f}", vs_color)
+
+    print(f"{Colors.HEADER}{Colors.BOLD}╚{border}╝{Colors.ENDC}")

requirements.txt

@@ -1,2 +1,3 @@
 numpy
 pandas
+requests

test_bitcoin.py

@@ -2,6 +2,7 @@
 from unittest.mock import patch
 from bitcoin import get_bitcoin_price, calculate_value
 
+
 # Test 1: Verify the calculation logic
 def test_calculate_value():
     """Ensure BTC to USD conversion math is correct."""
@@ -10,10 +11,12 @@ def test_calculate_value():
     expected = 125000.0
     assert calculate_value(amount, price) == expected
 
+
 # Test 2: Verify handling of zero amount
 def test_calculate_value_zero():
     assert calculate_value(0, 50000.0) == 0.0
 
+
 # Test 3: Mocking an API response
 @patch('bitcoin.requests.get')
 def test_get_bitcoin_price(mock_get):
@@ -23,10 +26,11 @@ def test_get_bitcoin_price(mock_get):
         "bpi": {"USD": {"rate_float": 62000.50}}
     }
     mock_get.return_value.status_code = 200
-    
+
     price = get_bitcoin_price()
     assert price == 62000.50
 
+
 # Test 4: Handling API failure
 @patch('bitcoin.requests.get')
 def test_get_price_api_error(mock_get):

test_bitcoin_trading.py

@@ -13,7 +13,7 @@ def reset_colors():
         'HEADER': Colors.HEADER,
         'BLUE': Colors.BLUE,
         'GREEN': Colors.GREEN,
-        'RED': Colors.RED,
+        'FAIL': Colors.FAIL,
         'ENDC': Colors.ENDC,
         'BOLD': Colors.BOLD,
     }
@@ -22,7 +22,7 @@ def reset_colors():
     Colors.HEADER = original_colors['HEADER']
     Colors.BLUE = original_colors['BLUE']
     Colors.GREEN = original_colors['GREEN']
-    Colors.RED = original_colors['RED']
+    Colors.FAIL = original_colors['FAIL']
     Colors.ENDC = original_colors['ENDC']
     Colors.BOLD = original_colors['BOLD']
 
@@ -58,7 +58,7 @@ def test_colors_disable(reset_colors):
     Colors.disable()
     assert Colors.HEADER == ""
     assert Colors.GREEN == ""
-    assert Colors.RED == ""
+    assert Colors.FAIL == ""
 
 
 def test_simulation_integration():

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 = {