### Example Calculation

Source: https://tradingtune.com/glossary

Demonstrates a simple calculation for win rate and average profit/loss.

```python
0.58 * 120 - 0.42 * 95  # 29.7
```

--------------------------------

### Calculate Sharpe Ratio

Source: https://tradingtune.com/glossary

Calculates the average periodic return per unit of return volatility. Requires the 'statistics' module for mean and standard deviation.

```Python
from statistics import mean, pstdev

# Return earned per unit of volatility, on periodic (e.g. daily) returns.
def sharpe(returns, risk_free=0.0):
    excess = [r - risk_free for r in returns]
    return mean(excess) / pstdev(excess)
```

--------------------------------

### Calculate Maximum Drawdown

Source: https://tradingtune.com/glossary

Calculates the largest peak-to-trough equity drop as a fraction of the prior peak. Assumes equity is a list of values.

```Python
# Largest peak-to-trough drop in equity, as a fraction of the prior peak.
def max_drawdown(equity):
    peak, mdd = equity[0], 0.0
    for value in equity:
        peak = max(peak, value)
        mdd = max(mdd, (peak - value) / peak)
    return mdd

max_drawdown([100, 120, 90, 130, 110])  # 0.25
```

--------------------------------

### Calculate Win Rate

Source: https://tradingtune.com/glossary

Calculates the share of closed trades that ended in profit. The value ranges from 0 to 1.

```Python
# Share of closed trades that ended in profit (0 to 1).
win_rate = winning_trades / total_trades

# Example: 82 winners out of 142 trades
82 / 142  # 0.577
```

--------------------------------

### Calculate Profit Factor

Source: https://tradingtune.com/glossary

Calculates the gross profit earned per dollar of gross loss. A value above 1 indicates net positive performance.

```Python
# Gross profit per unit of gross loss. Above 1 is net positive.
profit_factor = gross_profit / abs(gross_loss)

# Example: 5400 in winners against 2850 in losers
5400 / 2850  # 1.89
```

--------------------------------

### Calculate Expectancy Per Trade

Source: https://tradingtune.com/glossary

Calculates the average profit or loss per trade, weighted by the frequency of wins and losses.

```Python
# Average profit (or loss) per trade.
expectancy = win_rate * avg_win - (1 - win_rate) * avg_loss
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.