Added intraday FX total returns

README.md

@@ -166,6 +166,9 @@ In finmarketpy/examples you will find several examples, including some simple tr
 
 # finmarketpy log
 
+* 22 Jan 2021
+ * FX spot total returns now supports intraday data and added example
+ * Fixed problem with Numba implementation of FX spot total returns
 * 17 Jan 2021
  * Fix vol surface examples to work with new FXVolSurface
 * 16 Jan 2021

finmarketpy/curve/fxspotcurve.py

@@ -23,7 +23,7 @@
 
 market_constants = MarketConstants()
 
-@guvectorize(['void(f8[:], f8[:], f8[:], f8[:], intp, intp, f8[:])'],
+@guvectorize(['void(f8[:], f8[:], f8[:], f8[:], f8, f8, f8[:])'],
  '(n),(n),(n),(n),(),()->(n)', cache=True, target="cpu", nopython=True)
 def _spot_index_numba(spot, time_diff, base_deposit, terms_deposit, base_daycount, terms_daycount, out):
 
@@ -39,7 +39,7 @@ def _spot_index_numba(spot, time_diff, base_deposit, terms_deposit, base_daycoun
 def _spot_index(spot, time_diff, base_deposit, terms_deposit, base_daycount, terms_daycount):
  import numpy as np
 
- out = np.zero((len(spot)))
+ out = np.zeros((len(spot)))
  out[0] = 100
 
  for i in range(1, len(out)):
@@ -51,10 +51,6 @@ def _spot_index(spot, time_diff, base_deposit, terms_deposit, base_daycount, ter
 
  return out
 
-
-def _spot_index():
- pass
-
 class FXSpotCurve(object):
  """Construct total return (spot) indices for FX. In future will also convert assets from local currency to foreign currency
  denomination and construct indices from forwards series.
@@ -110,7 +106,7 @@ def fetch_continuous_time_series(self, md_request, market_data_generator, depo_t
  spot_df = market.fetch_market(md_request_download)
 
  return self.construct_total_return_index(md_request.tickers,
- self._calculations.pandas_outer_join([spot_df, depo_df]), tenor=depo_tenor,
+ self._calculations.pandas_outer_join([spot_df, depo_df]), depo_tenor=depo_tenor,
  output_calculation_fields=output_calculation_fields)
  else:
  # eg. we calculate via your domestic currency such as USD, so returns will be in your domestic currency
@@ -217,26 +213,30 @@ def construct_total_return_index(self, cross_fx, market_df, depo_tenor=None, out
  carry = carry.fillna(method='bfill')
 
  spot = spot[cross + ".close"].to_frame()
- base_deposit = carry[base_deposit.columns]
- terms_deposit = carry[terms_deposit.columns]
 
- # Calculate the time difference between each data point
- spot['index_col'] = spot.index
+ spot_vals = spot[cross + ".close"].values
+ base_deposit_vals = carry[cross[0:3] + depo_tenor + ".close"].values
+ terms_deposit_vals = carry[cross[3:6] + depo_tenor + ".close"].values
+
+ # Calculate the time difference between each data point (flooring it to whole days, because carry
+ # is accured when there's a new day)
+ spot['index_col'] = spot.index.floor('D')
  time = spot['index_col'].diff()
  spot = spot.drop('index_col', 1)
 
  time_diff = time.values.astype(float) / 86400000000000.0 # get time difference in days
+ time_diff[0] = 0.0
 
+ # Use Numba to do total return index calculation given has many loops
  total_return_index_df = pd.DataFrame(index=spot.index, columns=[cross + "-tot.close"],
- data=_spot_index_numba(spot.values, time_diff, base_deposit.values, terms_deposit.values,
+ data=_spot_index_numba(spot_vals, time_diff, base_deposit_vals, terms_deposit_vals,
  base_daycount, terms_daycount))
 
  if output_calculation_fields:
  total_return_index_df[cross + '-carry.close'] = carry
  total_return_index_df[cross + '-tot-return.close'] = total_return_index_df / total_return_index_df.shift(1) - 1.0
- total_return_index_df[cross + '-spot-return.close'] =  spot / spot.shift(1) - 1.0
+ total_return_index_df[cross + '-spot-return.close'] = spot / spot.shift(1) - 1.0
 
- # Use Numba to do total return index calculation given has many loops
  total_return_index_df_agg.append(total_return_index_df)
 
  return self._calculations.pandas_outer_join(total_return_index_df_agg)

finmarketpy_examples/fx_options_indices_examples.py

@@ -45,9 +45,8 @@
 # Choose run_example = 0 for everything
 # run_example = 1 - create total return index AUDUSD 1M long calls (and separately long puts) over 2008 financial crisis and further
 # run_example = 2 - create total return index USDJPY 1W short straddles over a long sample
-# run_example = 3 - create total return index USDJPY 1W short straddles (only selling on the last day of every month)
 
-run_example = 2
+run_example = 0
 
 def prepare_indices(cross, df_option_tot=None, df_option_tc=None, df_spot_tot=None):
  df_list = []

finmarketpy_examples/fx_spot_indices_examples.py

@@ -16,6 +16,8 @@
 Shows how to use finmarketpy to create total return indices for FX spot (ie. calculates spot returns + carry returns)
 """
 
+import pandas as pd
+
 # For plotting
 from chartpy import Chart, Style
 
@@ -32,7 +34,8 @@
 calculations = Calculations()
 
 # Choose run_example = 0 for everything
-# run_example = 1 - create total return indices from FX spot data + deposit for AUDJPY, and compare
+# run_example = 1 - create daily total return indices from FX spot data + deposit for AUDJPY, and compare
+# run_example = 2 - create intraday total return indices from FX spot data + deposit for GBPUSD, and compare with daily
 
 run_example = 0
 
@@ -76,4 +79,64 @@
  df = calculations.pandas_outer_join([df_tot, df_bbg_tot, df_spot, df_bbg_tot_forwards]).fillna(method='ffill')
  df = calculations.create_mult_index_from_prices(df)
 
+ chart.plot(df)
+
+###### Create total return indices plot for GBPUSD with intraday and daily data (from perspective of a USD investor)
+###### Compare intraday and daily total return indices
+if run_example == 2 or run_example == 0:
+
+ import pytz
+
+ # Get GBPUSD total returns from perspective of USD investor (via GBP and USD rates)
+ md_request = MarketDataRequest(start_date='01 Jan 2019', finish_date='01 Jul 2019',
+ data_source='bloomberg', cut='NYC', category='fx',
+ tickers=['GBPUSD'],
+ cache_algo='cache_algo_return',
+ abstract_curve=FXSpotCurve(construct_via_currency='USD', depo_tenor='ON'))
+
+ df_tot = market.fetch_market(md_request=md_request)
+ df_tot.columns = [x + '-tot-cuemacro' for x in df_tot.columns]
+ df_tot = df_tot.tz_localize(pytz.utc)
+ df_tot.index = df_tot.index + pd.Timedelta(hours=22) # Roughly NY close 2200 GMT
+
+ md_request.abstract_curve = None
+
+ # Get intraday spot data
+ md_request.freq = 'tick'
+ md_request.data_source = 'dukascopy'
+
+ df_intraday_spot = market.fetch_market(md_request=md_request)
+ df_intraday_spot = pd.DataFrame(df_intraday_spot.resample('1min').last().dropna())
+
+ # Get Bloomberg calculated total return indices (for spot)
+ md_request.category = 'fx-tot'
+ md_request.freq = 'daily'
+ md_request.data_source = 'bloomberg'
+
+ df_bbg_tot = market.fetch_market(md_request)
+ df_bbg_tot.columns = [x + '-bbg' for x in df_bbg_tot.columns]
+ df_bbg_tot = df_bbg_tot.tz_localize(pytz.utc)
+ df_bbg_tot.index = df_bbg_tot.index + pd.Timedelta(hours=22) # Roughly NY close 2200 GMT
+
+ md_request = MarketDataRequest(start_date='01 Jan 2019', finish_date='01 Jul 2019',
+ data_source='bloomberg', cut='NYC', category='base-depos',
+ tickers=['GBPON', 'USDON'],
+ cache_algo='cache_algo_return')
+
+ # Join daily deposit data with intraday spot data
+ # OK to fill down, because deposit data isn't very volatile
+ df_deposit_rates = market.fetch_market(md_request).tz_localize(pytz.utc)
+
+ df_intraday_market = df_intraday_spot.join(df_deposit_rates, how='left')
+ df_intraday_market = df_intraday_market.fillna(method='ffill').fillna(method='bfill')
+
+ df_intraday_tot = FXSpotCurve().construct_total_return_index('GBPUSD', df_intraday_market, depo_tenor='ON')
+
+ # df_intraday_spot.columns = [x + '-intraday-spot' for x in df_intraday_spot.columns]
+ df_intraday_tot.columns = [x + '-intraday-tot' for x in df_intraday_spot.columns]
+
+ # Combine into a single data frame and plot
+ df = calculations.pandas_outer_join([df_bbg_tot, df_tot, df_intraday_tot]).fillna(method='ffill')
+ df = calculations.create_mult_index_from_prices(df)
+
  chart.plot(df)