Start to implement testPolicy

manual_strategy/TheoreticallyOptimalStrategy.py

@@ -1,18 +1,17 @@
 import pandas as pd
-import datetime as dt
+from util import get_data
-from util import get_data, plot_data
+from marketsim.marketsim import compute_portvals
+from optimize_something.optimization import calculate_stats
 
 
 def author():
     return "felixm"
 
 
-def main():
+def testPolicy(symbol, sd, ed, sv):
-    start_date = dt.datetime(2008, 1, 1)
+    print(f"{symbol=} {sd} - {ed} {sv=}")
-    end_date = dt.datetime(2009, 12, 31)
+    # trade = date, shares (-2000, -1000, 0, 1000, 2000)
-    prices = get_data(['JPM'], pd.date_range(start_date, end_date))
+    prices = get_data([symbol], pd.date_range(sd, ed))
-    print(prices)
+    print(prices.index)
+    return
 
-
-if __name__ == "__main__":
-    main()

manual_strategy/indicators.py

@@ -1,5 +1,37 @@
-
+import datetime as dt
+import TheoreticallyOptimalStrategy as tos
 
 
 def author():
     return "felixm"
+
+
+def test_policy():
+    sd = dt.datetime(2008, 1, 1)
+    ed = dt.datetime(2009, 12, 31)
+    tos.testPolicy(symbol="JPM", sd=sd, ed=ed, sv=100000)
+
+
+def normalize(timeseries):
+    return timeseries / timeseries.iloc[0]
+
+
+def bollinger_band(prices):
+    pass
+
+
+def main():
+    test_policy()
+    # sd = dt.datetime(2008, 1, 1)
+    # ed = dt.datetime(2009, 12, 31)
+    # prices = get_data(['JPM'], pd.date_range(sd, ed))
+    # prices['JPM'] = normalize(prices['JPM'])
+    # print(prices)
+
+    # plot_data(prices)
+    # prices_appl = get_data(['AAPL'], pd.date_range(sd, ed), 'High')
+    # prices['AAPL'] = prices_appl['AAPL']
+
+
+if __name__ == "__main__":
+    main()