def get_daily_close(self):
grouped_trade_price_last = self.matched_orders[["DateTime", self.active_fill_price_col]].groupby(
pd.Grouper(key="DateTime",
freq='B'
), sort=False
).last()
grouped_trade_price_last["DateTime"] = grouped_trade_price_last.index
grouped_trade_price_last.reset_index(drop=True, inplace = True)
grouped_trade_price_last.rename(columns={self.active_fill_price_col: "price"}, inplace = True)
return timemethods.date_time_to_sim_time(grouped_trade_price_last)
def get_relative_trade_price_range(self, timewindow):
grouped_trade_price = self.matched_orders[["DateTime", self.active_fill_price_col]].groupby(
pd.Grouper(key="DateTime", freq=timewindow))
grouped_trade_price_max = grouped_trade_price.max()
grouped_trade_price_min = grouped_trade_price.min()
rel_range = (grouped_trade_price_max - grouped_trade_price_min) / grouped_trade_price_min
rel_range["DateTime"] = rel_range.index
rel_range.reset_index(drop=True, inplace = True)
rel_range.rename(columns={self.active_fill_price_col: "rel_range"}, inplace = True)
return timemethods.date_time_to_sim_time(rel_range)
def _get_resampled_data_by_active_agent_name(self, agent_name, col_name,sample_freq):
unsampled_data = self.get_active_agent_data(agent_name)
unsampled_data = unsampled_data[["DateTime", col_name]] #select the col we are interested in
unsampled_data = unsampled_data.groupby(["DateTime"]).agg({"DateTime" : "last",col_name:"sum"}) # sum any duplicates
resampled_data = unsampled_data.groupby(pd.Grouper(key="DateTime",
freq='B'
), sort=False
).resample(sample_freq ,on = "DateTime",).sum().reset_index(level = 0,
drop= True)
resampled_data["DateTime"] = resampled_data.index
resampled_data = resampled_data.rename(columns={col_name: agent_name})
return timemethods.date_time_to_sim_time(resampled_data)
def get_last_traded_price(self, timewindow):
grouped_trade_price_last = self.matched_orders[["DateTime", self.active_fill_price_col]].groupby(
pd.Grouper(key="DateTime",
freq='B'
), sort = False
).resample(timewindow,
on = "DateTime"
).last().reset_index(level=0,
drop = True)
grouped_trade_price_last["DateTime"] = grouped_trade_price_last.index
grouped_trade_price_last.reset_index(drop=True, inplace = True)
grouped_trade_price_last.rename(columns={self.active_fill_price_col: "price"}, inplace = True)
return timemethods.date_time_to_sim_time(grouped_trade_price_last)
def get_volume_to_side_by_agent(matched_orders, agent_name, side,sample_freq = "1T"):
vol_to_side = matched_orders[(matched_orders["active_agent"].str.contains(agent_name)) & (matched_orders[" active_side"] == side)][
["DateTime", " passive_fillQty"]].groupby(pd.Grouper(key="DateTime",freq = 'B')).resample(sample_freq,on = "DateTime",).sum().reset_index(level=0,
drop=True)
vol_to_side["DateTime"] = vol_to_side.index
return timemethods.date_time_to_sim_time(vol_to_side)