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| -rw-r--r-- | python/mark_backtest_underpar.py | 31 |
1 files changed, 31 insertions, 0 deletions
diff --git a/python/mark_backtest_underpar.py b/python/mark_backtest_underpar.py new file mode 100644 index 00000000..863af8c7 --- /dev/null +++ b/python/mark_backtest_underpar.py @@ -0,0 +1,31 @@ +import pandas as pd +from db import dbengine +import matplotlib +import numpy as np +import matplotlib.pyplot as plt +import statsmodels.api as sm +from statsmodels.formula.api import gls +import seaborn as sb + +df = pd.read_sql_table('external_marks_mapped',dbengine('dawndb'), + parse_dates=['date']) +df = df[df.source.notnull()] +df_wide = (pd.pivot_table(df, 'mark', ['identifier', 'date'], 'source'). + reset_index(). + sort_values('date')) +df_trades = pd.read_sql_query("select trade_date, identifier, principal_payment, price " \ + "from bonds where buysell = 'f'" + ,dbengine('dawndb'), + parse_dates=['trade_date']) +df_trades = df_trades.sort_values('trade_date') +df_wide = pd.merge_asof(df_trades, df_wide, left_on='trade_date', right_on='date', by='identifier') +df_long = pd.merge_asof(df_trades, df, left_on='trade_date', right_on='date', by='identifier') + +#pretty plot +sns.lmplot(x='mark', y='price', hue = 'source', data=df_long, fit_reg=False) + + +#fit all the models at once +params = (df_long. + groupby('source'). + apply(lambda df: gls('price~mark', df).fit().params) |