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=1000, coef=1, t_rate=1 + 0.02 / 12) # startDate = datetime.datetime.fromisoformat("2000-01-01") # endDate = datetime.datetime.fromisoformat("2022-01-01") print(stockList[0]) stockData = lib.get_data(stockList[0]) start_date = stockData.index[0] year_step = 5 label_string = "Investing to {} monthly over {} years, increasing {:.2f}%, starting from ${}".format( stockList[0], year_step, (monthly_params["coef"] * 100) - 100, monthly_params["sum"], ) print(label_string) plot_param = "annual%" strategies = ( st.DCA, st.QDCA, st.SmaCross, ) df_comp = pd.DataFrame(index=list(st.__name__ for st in strategies)) df_vwr = pd.DataFrame(index=list(st.__name__ for st in strategies)) 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) 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 strategies ] ) print(newdf.to_string()) df_comp[start_year] = newdf[plot_param].values # print(df[["annual%", "froi%", "cost", "total_value", "max_dd%", "max_md"]]) # print(newdf["annual%"]) print(df_comp.to_string()) df_comp.T.plot() plt.grid() plt.title(label_string) plt.ylabel(plot_param) plt.xlabel("year") plt.show()