Added the ability for the backtester to use unrealised PnL from the Position objects to calculate a tick-by-tick equity curve. Added a performance directory that calculates drawdown statistics. Modified the output.py script to use Seaborn and output the equity curve, returns and drawdown curve.

performance/performance.py

@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+
+
+def create_drawdowns(pnl):
+    """
+    Calculate the largest peak-to-trough drawdown of the PnL curve
+    as well as the duration of the drawdown. Requires that the 
+    pnl_returns is a pandas Series.
+
+    Parameters:
+    pnl - A pandas Series representing period percentage returns.
+
+    Returns:
+    drawdown, duration - Highest peak-to-trough drawdown and duration.
+    """
+
+    # Calculate the cumulative returns curve 
+    # and set up the High Water Mark
+    hwm = [0]
+
+    # Create the drawdown and duration series
+    idx = pnl.index
+    drawdown = pd.Series(index = idx)
+    duration = pd.Series(index = idx)
+
+    # Loop over the index range
+    for t in range(1, len(idx)):
+        hwm.append(max(hwm[t-1], pnl[t]))
+        drawdown[t]= (hwm[t]-pnl[t])
+        duration[t]= (0 if drawdown[t] == 0 else duration[t-1]+1)
+    return drawdown, drawdown.max(), duration.max()