def get_first_n_nanos(df: dd, nanos: int):
start_time = df.iloc[0]['time']
end_time = DataUtils.date_to_unix(start_time, 'ns') + nanos
converted = df['time'].apply(lambda x: DataUtils.date_to_unix(x, 'ns'))
return df[converted < end_time]
def get_last_n_nanos(df: dd, nanos: int):
end_time = df.iloc[0]['time']
start_time = DataUtils.date_to_unix(end_time, 'ns') + nanos
converted = df['time'].apply(lambda x: DataUtils.date_to_unix(x, 'ns'))
return df[start_time > converted]
def __graph_price_time_set(df: dd, marker: str):
y = df['price'].astype('float64').fillna(method='ffill')
times = df['time'].astype('datetime64[ns]').apply(lambda x: DataUtils.date_to_unix(x, 'ns'))
start_time = times.min()
x = times.apply(lambda z: (z - start_time) / 1e9)
self.config.plt.plot(x, y, marker)
def graph_interval(self, orders_df: dd):
order_time_delta_df = orders_df['time'].apply(lambda x: DataUtils.date_to_unix(x, 'ns') / 1e6).diff()
self.logger.debug(order_time_delta_df)
cleaned_df = order_time_delta_df[order_time_delta_df != 0]
self.graph_distribution(cleaned_df, self.data_description + ' inter-order arrival times',
'inter order time (ms)', bins=100)
def get_time_intervals(df):
intervals = df['time'].apply(
lambda x: DataUtils.date_to_unix(x, 'ns') / 1e6).diff()
cleaned_intervals = intervals[intervals != 0].dropna()
return cleaned_intervals