Analyzing Results

time,strategy,previous_ticker,previous_result,buy_ticker,buy_side,stake_usd,price_usd,contracts,fee_usd,gross_profit_usd,outcome,payout_usd,profit_usd
2025-03-05T14:23:58.123456+00:00,PREVIOUS,KXBTC15M-25MAR05-T1015,yes,KXBTC15M-25MAR05-T1030,yes,5.0,0.52,9.6154,,,WIN,9.6154,4.6154
2025-03-05T14:24:03.234567+00:00,MOMENTUM,KXBTC15M-25MAR05-T1015,BTC +0.234%,KXBTC15M-25MAR05-T1030,yes,5.0,0.52,9.6154,,,WIN,9.6154,4.6154
2025-03-05T14:24:08.345678+00:00,CONSENSUS,,PREV=yes MOM=yes,KXBTC15M-25MAR05-T1030,yes,4.8,0.48,10.0,0.048,5.2,WIN,10.0,5.152

time,buy_ticker,buy_side,stake_usd,price_usd,contracts,order_id,prev_signal,mom_signal,outcome,payout_usd,profit_usd
2025-03-05T14:30:45.123456+00:00,KXBTC15M-25MAR05-T1045,yes,4.8,0.48,10,DRY-RUN-f8e9a7b6,yes,yes,WIN,10.0,5.2
2025-03-05T14:45:32.234567+00:00,KXBTC15M-25MAR05-T1100,no,5.2,0.52,10,DRY-RUN-a1b2c3d4,no,no,LOSS,0.0,-5.2

python analyze.py

Loaded 150 trades from data/mock_trades.csv
Note: 12 trades are pending (no outcome yet)

======================================================================
STRATEGY PERFORMANCE ANALYSIS
======================================================================

Strategy         Wins  Losses  Total   Win Rate      Profit        ROI
----------------------------------------------------------------------
CONSENSUS_2        24       7     31      77.4%     $+84.20      168.4%
CONSENSUS          22       8     30      73.3%     $+68.50      137.0%
ARBITRAGE          28       6     34      82.4%     $+21.50       31.6%
PREVIOUS_2         16      11     27      59.3%     $+31.00       62.0%
MOMENTUM           18      14     32      56.3%     $+42.00       84.0%
MOMENTUM_15        14      12     26      53.8%     $+15.00       30.0%
PREVIOUS           12      15     27      44.4%     $-23.50      -47.0%
----------------------------------------------------------------------

TOTALS             134     73    207      64.7%    $+239.70       79.9%

Best Strategy by Win Rate: ARBITRAGE (82.4%)
Best Strategy by Profit: CONSENSUS_2 ($84.20)
Best Strategy by ROI: CONSENSUS_2 (168.4%)

======================================================================

DETAILED BREAKDOWN
----------------------------------------------------------------------

ARBITRAGE:
  Total Trades: 34
  Wins: 28, Losses: 6
  Win Rate: 82.35%
  Total Staked: $68.00
  Total Profit: $21.50
  ROI: 31.62%
  Avg Profit/Trade: $0.63

CONSENSUS:
  Total Trades: 30
  Wins: 22, Losses: 8
  Win Rate: 73.33%
  Total Staked: $50.00
  Total Profit: $68.50
  ROI: 137.00%
  Avg Profit/Trade: $2.28

CONSENSUS_2:
  Total Trades: 31
  Wins: 24, Losses: 7
  Win Rate: 77.42%
  Total Staked: $50.00
  Total Profit: $84.20
  ROI: 168.40%
  Avg Profit/Trade: $2.72

[... additional strategies ...]

import pandas as pd

# Load trades
df = pd.read_csv('data/mock_trades.csv')

# Filter to settled trades only
settled = df[df['outcome'].notna() & (df['outcome'] != '')]

# Calculate win rate by strategy
win_rates = settled.groupby('strategy')['outcome'].apply(
    lambda x: (x == 'WIN').sum() / len(x) * 100
)
print(win_rates)

# Calculate cumulative P&L over time
settled['cumulative_pnl'] = settled['profit_usd'].astype(float).cumsum()

# Plot performance
import matplotlib.pyplot as plt
settled.plot(x='time', y='cumulative_pnl', figsize=(12, 6))
plt.title('Cumulative P&L Over Time')
plt.ylabel('Profit/Loss (USD)')
plt.show()

import numpy as np

# Calculate returns per trade
df['return'] = df['profit_usd'] / df['stake_usd']

# Sharpe ratio (assuming risk-free rate = 0)
sharpe = df['return'].mean() / df['return'].std() * np.sqrt(len(df))
print(f"Sharpe Ratio: {sharpe:.2f}")

# Calculate cumulative P&L
df['cumulative'] = df['profit_usd'].cumsum()

# Calculate running maximum
running_max = df['cumulative'].cummax()

# Calculate drawdown
drawdown = df['cumulative'] - running_max
max_drawdown = drawdown.min()

print(f"Maximum Drawdown: ${max_drawdown:.2f}")

# Create win/loss indicator
df['is_win'] = (df['outcome'] == 'WIN').astype(int)

# Calculate streaks
streaks = df['is_win'].groupby(
    (df['is_win'] != df['is_win'].shift()).cumsum()
).agg(['sum', 'count'])

max_win_streak = streaks[streaks['sum'] > 0]['count'].max()
max_loss_streak = streaks[streaks['sum'] == 0]['count'].max()

print(f"Longest win streak: {max_win_streak}")
print(f"Longest loss streak: {max_loss_streak}")

import pandas as pd

df = pd.read_csv('data/mock_trades.csv')

# Create Excel writer
with pd.ExcelWriter('analysis.xlsx') as writer:
    # All trades
    df.to_excel(writer, sheet_name='All Trades', index=False)
    
    # Summary by strategy
    summary = df.groupby('strategy').agg({
        'profit_usd': ['sum', 'mean', 'count'],
        'outcome': lambda x: (x == 'WIN').sum() / len(x) * 100
    })
    summary.to_excel(writer, sheet_name='Summary')

import pandas as pd
import json

df = pd.read_csv('data/mock_trades.csv')

# Convert to JSON
result = df.to_json(orient='records', date_format='iso')

with open('trades.json', 'w') as f:
    f.write(result)

import pandas as pd
import time

WARNING_LOSS_THRESHOLD = -50.0  # Alert if loss exceeds this

while True:
    df = pd.read_csv('data/mock_trades.csv')
    total_pnl = df['profit_usd'].sum()
    
    if total_pnl < WARNING_LOSS_THRESHOLD:
        print(f"⚠️  WARNING: Total P&L is ${total_pnl:.2f}")
        # Send email, SMS, etc.
    
    time.sleep(60)  # Check every minute