diff --git a/backtester/statistics/__init__.py b/backtester/statistics/__init__.py
index 5cfc470..6262f3c 100644
--- a/backtester/statistics/__init__.py
+++ b/backtester/statistics/__init__.py
@@ -1 +1,2 @@
 from .charts import monthly_returns_heatmap, returns_histogram, returns_chart
+from .stats import summary
diff --git a/backtester/statistics/stats.py b/backtester/statistics/stats.py
new file mode 100644
index 0000000..5f2dbfb
--- /dev/null
+++ b/backtester/statistics/stats.py
@@ -0,0 +1,76 @@
+import pandas as pd
+import numpy as np
+
+from ..enums import Order
+
+
+def summary(trade_log, balance):
+    """Returns a table with summary statistics about the trade log"""
+
+    initial_capital = balance['total capital'].get(0)
+    trade_log.loc[:,
+                  ('totals',
+                   'capital')] = (-trade_log['totals']['cost'] * trade_log['totals']['qty']).cumsum() + initial_capital
+
+    daily_returns = balance['% change'] * 100
+
+    first_leg = trade_log.columns.levels[0][0]
+
+    entry_mask = trade_log[first_leg].eval('(order == @Order.BTO) | (order == @Order.STO)')
+    entries = trade_log.loc[entry_mask]
+    exits = trade_log.loc[~entry_mask]
+
+    costs = np.array([])
+    for contract in entries[first_leg]['contract']:
+        entry = entries.loc[entries[first_leg]['contract'] == contract]
+        exit_ = exits.loc[exits[first_leg]['contract'] == contract]
+        try:
+            # Here we assume we are entering only once per contract (i.e both entry and exit_ have only one row)
+            costs = np.append(costs, (entry['totals']['cost'] * entry['totals']['qty']).values[0] +
+                              (exit_['totals']['cost'] * exit_['totals']['qty']).values[0])
+        except IndexError:
+            continue
+
+    wins = costs < 0
+    losses = costs >= 0
+    profit_factor = np.sum(wins) / np.sum(losses)
+    total_trades = len(exits)
+    win_number = np.sum(wins)
+    loss_number = total_trades - win_number
+    win_pct = (win_number / total_trades) * 100
+    largest_loss = max(0, np.max(costs))
+    avg_profit = np.mean(-costs)
+    avg_pl = np.mean(daily_returns)
+    total_pl = (trade_log['totals']['capital'].iloc[-1] / initial_capital) * 100
+
+    data = [total_trades, win_number, loss_number, win_pct, largest_loss, profit_factor, avg_profit, avg_pl, total_pl]
+    stats = [
+        'Total trades', 'Number of wins', 'Number of losses', 'Win %', 'Largest loss', 'Profit factor',
+        'Average profit', 'Average P&L %', 'Total P&L %'
+    ]
+    strat = ['Strategy']
+    summary = pd.DataFrame(data, stats, strat)
+
+    # Applies formatters to rows
+    def format_row_wise(styler, formatters):
+        for row, row_formatter in formatters.items():
+            row_num = styler.index.get_loc(row)
+
+            for col_num in range(len(styler.columns)):
+                styler._display_funcs[(row_num, col_num)] = row_formatter
+
+        return styler
+
+    formatters = {
+        "Total trades": lambda x: f"{x:.0f}",
+        "Number of wins": lambda x: f"{x:.0f}",
+        "Number of losses": lambda x: f"{x:.0f}",
+        "Win %": lambda x: f"{x:.2f}%",
+        "Largest loss": lambda x: f"${x:.2f}",
+        "Profit factor": lambda x: f"{x:.2f}",
+        "Average profit": lambda x: f"${x:.2f}",
+        "Average P&L %": lambda x: f"{x:.2f}%",
+        "Total P&L %": lambda x: f"{x:.2f}%"
+    }
+
+    return format_row_wise(summary.style, formatters)