1 files changed, 0 insertions, 89 deletions
diff --git a/python/exploration/swaption_calendar_spread.py b/python/exploration/swaption_calendar_spread.py
deleted file mode 100644
index e21ffd66..00000000
--- a/python/exploration/swaption_calendar_spread.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import sys
-#don't do this at home
-sys.path.append("..")
-from analytics import (Swaption, BlackSwaption, BlackSwaptionVolSurface,
-                       Index, ProbSurface, Portfolio)
-from analytics.scenarios import run_swaption_scenarios, run_index_scenarios, run_portfolio_scenarios
-from pandas.tseries.offsets import BDay
-import datetime
-import numpy as np
-import pandas as pd
-from scipy.interpolate import SmoothBivariateSpline
-
-import os
-import numpy as np
-import matplotlib
-import matplotlib.pyplot as plt
-from graphics import plot_time_color_map, plot_color_map
-
-from db import dbengine
-engine  = dbengine('serenitasdb')
-
-def plot_df(df, spread_shock, vol_shock, attr="pnl"):
-    val_date = df.index[0].date()
-    fig = plt.figure()
-
-    ax = fig.gca(projection='3d')
-    ## use smoothing spline on a finer grid
-    series = df[attr]
-    f = SmoothBivariateSpline(df.vol_shock.values, df.spread_shock.values, series.values)
-    xx, yy = np.meshgrid(vol_shock, spread_shock)
-    surf = ax.plot_surface(xx, yy, f(vol_shock, spread_shock).T, cmap=cm.viridis)
-    ax.set_xlabel("Volatility shock")
-    ax.set_ylabel("Spread")
-    ax.set_zlabel("PnL")
-    ax.set_title('{} of Trade on {}'.format(attr.title(), val_date))
-
-def plot_trade_scenarios(portf, shock_min=-.15, shock_max=.2, period=-1, vol_time_roll=True):
-    portf.reset_pv()
-    earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
-    date_range = pd.bdate_range(portf.indices[0].trade_date,
-                                earliest_date - BDay(), freq='3B')
-    vol_shock = np.arange(-0.15, 0.3, 0.01)
-    spread_shock = np.arange(shock_min, shock_max, 0.01)
-    index = portf.indices[0].name.split()[1]
-    series = portf.indices[0].name.split()[3][1:]
-    vs = BlackSwaptionVolSurface(index, series, trade_date=portf.indices[0].trade_date)
-    vol_surface = vs[vs.list(option_type='payer')[-1]]
-
-    df = run_portfolio_scenarios(portf, date_range, spread_shock, vol_shock, vol_surface,
-                                 params=["pnl","delta"])
-
-    hy_plot_range = 100 + (500 - portf.indices[0].spread * (1 + spread_shock)) * \
-                    abs(portf.indices[0].DV01) / portf.indices[0].notional * 100
-
-    shock =  hy_plot_range if index == 'HY' else portf.indices[0].spread * (1 + spread_shock)
-
-    plot_time_color_map(df[round(df.vol_shock,2)==0], shock, 'pnl', index=index)
-    plot_time_color_map(df[round(df.vol_shock,2)==.2], shock, 'pnl', index=index)
-    plot_color_map(df.loc[date_range[period]], shock, vol_shock, 'pnl', index=index)
-    return df
-
-def exercise_probability():
-    engine  = dbengine('serenitasdb')
-    #Ad hoc
-    option_delta = Index.from_name('HY', 29, '5yr')
-    option_delta.price = 107.875
-    option1 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 107, option_type="payer")
-    option2 = BlackSwaption(option_delta, datetime.date(2017, 12, 20), 105, option_type="payer")
-    option1.sigma = .280
-    option2.sigma = .371
-    option1.notional = 20_000_000
-    option2.notional = 40_000_000
-    option1.direction = 'Long'
-    option2.direction = 'Short'
-    option_delta.notional = option1.notional * option1.delta + option2.notional * option2.delta
-    option_delta.direction = 'Seller' if option_delta.notional > 0 else 'Buyer'
-    option_delta.notional = abs(option_delta.notional)
-    portf = Portfolio([option1, option2, option_delta])
-
-    portf.reset_pv()
-    earliest_date = min(portf.swaptions, key=lambda x: x.exercise_date).exercise_date
-    date_range = pd.bdate_range(portf.indices[0].trade_date, earliest_date - BDay(), freq='5B')
-    vol_shock = np.arange(-0.15, 0.3, 0.01)
-    spread_shock = np.arange(-0.15, 0.35, 0.01)
-    index = portf.indices[0].name.split()[1]
-    series = portf.indices[0].name.split()[3][1:]
-
-    vs = ProbSurface(index, series, trade_date=portf.indices[0].trade_date)
-    vs.plot(vs.list()[-1])