Implement theoretical optimal strategy and evaluate

manual_strategy/indicators.py

@@ -1,5 +1,9 @@
+import pandas as pd
 import datetime as dt
 import TheoreticallyOptimalStrategy as tos
+from util import plot_data, get_data
+from marketsim.marketsim import compute_portvals
+from optimize_something.optimization import calculate_stats
 
 
 def author():
@@ -7,10 +11,52 @@ def author():
 
 
 def test_policy():
+    symbol = "JPM"
+    start_value = 100000
     sd = dt.datetime(2008, 1, 1)
+    # ed = dt.datetime(2008, 1, 30)
     ed = dt.datetime(2009, 12, 31)
-    tos.testPolicy(symbol="JPM", sd=sd, ed=ed, sv=100000)
 
+    orders = tos.testPolicy(symbol=symbol, sd=sd, ed=ed, sv=start_value)
+    portvals = compute_portvals(orders, start_value, 0, 0)
+
+    start_date = portvals.index[0]
+    end_date = portvals.index[-1]
+    cum_ret, avg_daily_ret, \
+        std_daily_ret, sharpe_ratio = calculate_stats(portvals, [1])
+
+    d = {"Symbol": [symbol, symbol],
+         "Order": ["BUY", "SELL"],
+         "Shares": [1000, 1000]}
+    orders = pd.DataFrame(data=d, index=[start_date, end_date])
+    bench = compute_portvals(orders, start_value, 0, 0)
+    cum_ret_bench, avg_daily_ret_bench, \
+    std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
+
+    # Compare portfolio against benchmark
+    print(f"Date Range: {start_date} to {end_date}")
+    print()
+    print(f"Sharpe Ratio of Optimal Strategy: {sharpe_ratio}")
+    print(f"Sharpe Ratio of bench: {sharpe_ratio_bench}")
+    print()
+    print(f"Cumulative Return of Optimal Strategy: {cum_ret}")
+    print(f"Cumulative Return of bench: {cum_ret_bench}")
+    print()
+    print(f"Standard Deviation of Optimal Strategy: {std_daily_ret}")
+    print(f"Standard Deviation of bench: {std_daily_ret_bench}")
+    print()
+    print(f"Average Daily Return of Optimal Strategy: {avg_daily_ret}")
+    print(f"Average Daily Return of bench: {avg_daily_ret_bench}")
+    print()
+    print(f"Final Portfolio Value Optimal: {portvals.iloc[-1][0]}")
+    print(f"Final Portfolio Value Bench: {bench.iloc[-1][0]}")
+
+    portvals["Optimal"] = portvals["Portval"]
+    portvals["Bench"] = bench["Portval"]
+    portvals.drop(columns=["Portval"], inplace=True)
+
+    #XXX: Save to file instead.
+    plot_data(portvals)
 
 def normalize(timeseries):
     return timeseries / timeseries.iloc[0]
@@ -22,6 +68,8 @@ def bollinger_band(prices):
 
 def main():
     test_policy()
+
+    # plot_data(prices)
     # sd = dt.datetime(2008, 1, 1)
     # ed = dt.datetime(2009, 12, 31)
     # prices = get_data(['JPM'], pd.date_range(sd, ed))