some organizing

compare.py

@@ -1,9 +1,12 @@
 import datetime
 import lib
+from matplotlib import pyplot as plt
+import strategies as st
+import pandas as pd
 
 if __name__ == "__main__":
-    stockList = ["^GSPC"]
-    monthly_params = dict(sum=10000, coef=1.005, t_rate=1 + 0.02 / 12)
+    stockList = ["BRK-A"]
+    monthly_params = dict(sum=100000, coef=1.005, t_rate=1 + 0.02 / 12)
     # startDate = datetime.datetime.fromisoformat("2000-01-01")
     # endDate = datetime.datetime.fromisoformat("2022-01-01")
     print(stockList[0])
@@ -17,13 +20,35 @@ if __name__ == "__main__":
     period_years = 5
     stockData = lib.get_data(
         stockList[0],
-        datetime.datetime.fromisoformat("1928-01-01"),
-        datetime.datetime.now(),
     )
     start_date = stockData.index[0]
-    year_step = 5
+    year_step = 3
+    print(
+        "Investing monthly, increasing {:.2f}%, starting from ${}".format(
+            (monthly_params["coef"] * 100) - 100,
+            monthly_params["sum"],
+        )
+    )
     for start_year in range(start_date.year, datetime.datetime.today().year, year_step):
         start_date = datetime.date(start_year, 1, 1)
         end_date = datetime.date(start_year + year_step - 1, 12, 31)
+        if (today := datetime.date.today()) < end_date:
+            end_date = today
         stockData = lib.get_data(stockList[0], start_date, end_date)
-        lib.simulate(stockData, monthly_params)
+        start, end = lib.get_data_borders(stockData)
+        print(
+            "{} to {}, {:.1f} years".format(
+                start,
+                end,
+                (end - start).days / 365,
+            )
+        )
+        newdf = pd.concat(
+            [
+                lib.test_strategy(stockData, strategy, monthly_params)
+                for strategy in (st.DCA, st.QDCA)
+            ]
+        )
+        print(newdf.to_string())
+        # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]])
+        # print(newdf["annual%"])

lib.py

@@ -4,8 +4,26 @@ import backtrader as bt
 
 import strategies as st
 
+COLUMNS = [
+    "froi%",
+    "cost",
+    "total_value",
+    "#deals",
+    "#units",
+    "comms",
+    "monthly",
+    "annual%",
+    "max_dd_days",
+    "max_dd%",
+    "max_md",
+    "vwr",
+]
 # import data
-def get_data(stocks, start, end):
+def get_data(
+    stocks,
+    start=datetime.datetime.fromisoformat("1928-01-01"),
+    end=datetime.datetime.now(),
+):
     from pandas_datareader.yahoo.headers import DEFAULT_HEADERS
     from pandas_datareader import data as pdr
     import requests_cache
@@ -24,6 +42,12 @@ def get_data(stocks, start, end):
     return stockData.loc[start:end]
 
 
+def get_data_borders(stockData):
+    actual_start: datetime.datetime = stockData.index[0]
+    actual_end: datetime.datetime = stockData.index[-1]
+    return actual_start, actual_end
+
+
 def human_readable_size(size, decimal_places=3):
     for unit in ["$", "K$", "M$", "G$", "T$", "P$"]:
         if size < 1000.0 or unit == "P$":
@@ -58,57 +82,29 @@ def prepare_simulation(strategy, params, data, fund_mode=False):
     return cerebro
 
 
-def simulate(stockData, monthly_params):
+def test_strategy(stockData, strategy, monthly_params):
     pd.options.display.float_format = "{:,.2f}".format
-    df = pd.DataFrame(
-        columns=[
-            "froi%",
-            "cost",
-            "total_value",
-            "#deals",
-            "#units",
-            "comms",
-            "monthly",
-            "annual%",
-            "max_dd_days",
-            "max_dd%",
-            "max_md",
-            "vwr",
-        ]
-    )
-
-    actualStart: datetime.datetime = stockData.index[0]
-    actualEnd: datetime.datetime = stockData.index[-1]
+    df = pd.DataFrame(columns=COLUMNS)
+    actual_start, actual_end = get_data_borders(stockData)
     data = bt.feeds.PandasData(dataname=stockData)
 
-    for i, strategy in enumerate((st.DCA, st.QDCA, st.VA, st.QVA)):
-        cerebro = prepare_simulation(strategy, monthly_params, data)
-        therun = cerebro.run()[0]
-        # cerebro.plot(iplot=False, style="candlestick")
-        dd = therun.analyzers.drawdown
-        ret = therun.analyzers.returns
-        params = therun.calc_params()
-        # omg IM so sorry for this, ironically this is here to get human readable size
-        for i in 1, 2, 5, 6:
-            params[i] = human_readable_size(params[i])
-        annual = 100 * (
-            (1 + params[0] / 100) ** (365 / (actualEnd - actualStart).days) - 1
-        )
-        df.loc[strategy.__name__] = params + [
-            annual,
-            dd.get_analysis().max.len,
-            dd.get_analysis().max.drawdown,
-            human_readable_size(dd.get_analysis().max.moneydown),
-            ret.get_analysis()["vwr"],
-        ]
-    print(
-        "Investing monthly, increasing {:.2f}%, starting from ${}, from {} to {}, {:.1f} years".format(
-            (monthly_params["coef"] * 100) - 100,
-            monthly_params["sum"],
-            actualStart.date(),
-            actualEnd.date(),
-            (actualEnd - actualStart).days / 365,
-        )
+    cerebro = prepare_simulation(strategy, monthly_params, data)
+    therun = cerebro.run()[0]
+    # cerebro.plot(iplot=False, style="candlestick")
+    dd = therun.analyzers.drawdown
+    ret = therun.analyzers.returns
+    params = therun.calc_params()
+    # omg IM so sorry for this, ironically this is here to get human readable size
+    for i in 1, 2, 5, 6:
+        params[i] = human_readable_size(params[i])
+    annual = 100 * (
+        (1 + params[0] / 100) ** (365 / (actual_end - actual_start).days) - 1
     )
-    # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]])
-    print(df.to_string())
+    df.loc[strategy.__name__] = params + [
+        annual,
+        dd.get_analysis().max.len,
+        dd.get_analysis().max.drawdown,
+        human_readable_size(dd.get_analysis().max.moneydown),
+        ret.get_analysis()["vwr"],
+    ]
+    return df