diff options
Diffstat (limited to 'python/exploration/curve_trades.py')
| -rw-r--r-- | python/exploration/curve_trades.py | 38 |
1 files changed, 23 insertions, 15 deletions
diff --git a/python/exploration/curve_trades.py b/python/exploration/curve_trades.py index 353f5bbc..7a7d29c2 100644 --- a/python/exploration/curve_trades.py +++ b/python/exploration/curve_trades.py @@ -51,8 +51,9 @@ def ratio_within_series(index='IG', rolling=6, param='duration'): ratio = (df[param]. apply(lambda s: s / df[param]['5yr'].values, raw=True)) ratio.columns = pd.MultiIndex.from_product([[param + '_ratio_to_5yr'], ratio.columns]) - df = df.join(ratio) - return df.groupby(['date', 'index']).nth(-1) + df = df.join(ratio).groupby(['date']).tail(1) + df = df.reset_index(level=['index', 'version'], drop=True) + return df def curve_3_5_10(df): """ @@ -86,12 +87,12 @@ def curve_returns(index='IG', rolling=6): ## on-the-run returns returns = df.price_return.unstack(-1).dropna().groupby(level='date').nth(-1) - strategy = ['35','510', '710', '3510'] strategies_return = pd.DataFrame( - {'3-5': -.64 * returns['3yr'] + returns['5yr'], - '5-10': 1.78 * returns['5yr'] - returns['10yr'], + {'5-10': 1.78 * returns['5yr'] - returns['10yr'], '7-10': 1.33 * returns['7yr'] - returns['10yr'], - '3-5-10': -2 * returns['3yr'] + 3 * returns['5yr'] - returns['10yr']}) + '3-5-10': -2 * returns['3yr'] + 3 * returns['5yr'] - returns['10yr'], + '3-5': returns['5yr'] - 1.56 * returns['3yr'], + '3-7': returns['7yr'] - 2.07 * returns['3yr']}) strategies_return_monthly = (strategies_return. groupby(pd.TimeGrouper(freq='M')). agg(lambda df: (1 + df).prod() - 1)) @@ -152,21 +153,28 @@ def read_IG_curve_pos(): def curve_model(tenor_1='5yr', tenor_2='10yr'): #OLS model df = ratio_within_series(param='closespread') - df = df.groupby(level='date').last() - df = pd.concat([df.duration[tenor_1], df.closespread[tenor_1], - df.closespread_ratio_to_5yr[tenor_2]], axis=1, - keys=['duration', 'closespread', 'ratio']) - ols_model = smf.ols('np.log(ratio) ~ np.log(duration) + np.log(closespread)', data=df).fit() - df['predicted'] = np.exp(results.predict()) + df = pd.concat([df.duration[tenor_1], df.duration[tenor_2], + df.closespread[tenor_1], + df.closespread_ratio_to_5yr[tenor_2], + df.theta[tenor_1], df.theta[tenor_2]], + axis=1, + keys=['duration1', 'duration2', 'closespread', + 'ratio', 'theta1', 'theta2']) + df = np.log(df) + ols_model = smf.ols('ratio ~ closespread + duration1 + theta1 + theta2', + data=df).fit() return df, ols_model def curve_model_results(df, model): prstd_ols, df['down_2_stdev'], df['up_2_stdev'] = wls_prediction_std(model) #dr/dspread = exp(k) + spread_coeff * duration ^ dur_coeff * spread ^ (spread_coeff-1) - df['down_2_stdev'] = np.exp(df['down_2_stdev']) - df['up_2_stdev'] = np.exp(df['up_2_stdev']) + cols = ['ratio', 'closespread', 'down_2_stdev', 'up_2_stdev'] + df[cols] = np.exp(df[cols]) df['predicted'] = np.exp(model.predict()) - df['dr_dspread'] = np.exp(model.params[0]) * model.params[2] * df.duration ** model.params[1] * df.closespread ** (model.params[2] - 1) + df[['predicted', 'down_2_stdev', 'up_2_stdev']]=\ + df[['predicted', 'down_2_stdev', 'up_2_stdev']].multiply(df['closespread'].values, axis=0) + ax = df[['predicted', 'down_2_stdev', 'up_2_stdev']].reset_index(level='series', drop=True).plot() + df['dr_dspread'] = np.exp(model.params[0]) * model.params[2] * df.duration1 ** model.params[1] * df.closespread ** (model.params[2] - 1) return df def curve_var(): |