4 changed files with 0 additions and 264 deletions
@ -1,21 +0,0 @@ |
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import datetime |
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import math |
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import random |
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import time |
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from typing import List |
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from dateutil.relativedelta import relativedelta |
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|
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import pyfacts as pft |
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data = [ |
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("2021-01-01", 10), |
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("2021-02-01", 12), |
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("2021-03-01", 14), |
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("2021-04-01", 16), |
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("2021-05-01", 18), |
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("2021-06-01", 20), |
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] |
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|
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ts = pft.TimeSeries(data) |
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print(repr(ts)) |
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@ -1,118 +0,0 @@ |
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import datetime |
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import math |
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import random |
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|
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# import time |
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from typing import List |
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from dateutil.relativedelta import relativedelta |
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import pyfacts as pft |
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|
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def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list: |
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"""Generates a price following a geometric brownian motion process based on the input of the arguments. |
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|
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Since this function is used only to generate data for tests, the seed is fixed as 1234. |
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Many of the tests rely on exact values generated using this seed. |
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If the seed is changed, those tests will fail. |
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Parameters: |
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------------ |
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s0: float |
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Asset inital price. |
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|
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mu: float |
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Interest rate expressed annual terms. |
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|
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sigma: float |
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Volatility expressed annual terms. |
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num_prices: int |
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number of prices to generate |
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|
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Returns: |
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-------- |
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Returns a list of values generated using GBM algorithm |
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""" |
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random.seed(1234) # WARNING! Changing the seed will cause most tests to fail |
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all_values = [] |
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for _ in range(num_prices): |
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s0 *= math.exp( |
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(mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1) |
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) |
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all_values.append(round(s0, 2)) |
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return all_values |
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|
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def sample_data_generator( |
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frequency: pft.Frequency, |
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num: int = 1000, |
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skip_weekends: bool = False, |
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mu: float = 0.1, |
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sigma: float = 0.05, |
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eomonth: bool = False, |
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) -> List[tuple]: |
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"""Creates TimeSeries data |
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|
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Parameters: |
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----------- |
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frequency: Frequency |
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The frequency of the time series data to be generated. |
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num: int |
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Number of date: value pairs to be generated. |
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skip_weekends: bool |
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Whether weekends (saturday, sunday) should be skipped. |
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Gets used only if the frequency is daily. |
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mu: float |
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Mean return for the values. |
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sigma: float |
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standard deviation of the values. |
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|
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Returns: |
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-------- |
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Returns a TimeSeries object |
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""" |
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start_date = datetime.datetime(2017, 1, 1) |
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timedelta_dict = { |
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frequency.freq_type: int( |
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frequency.value * num * (7 / 5 if frequency == pft.AllFrequencies.D and skip_weekends else 1) |
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) |
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} |
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end_date = start_date + relativedelta(**timedelta_dict) |
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dates = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth) |
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values = create_prices(1000, mu, sigma, num) |
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ts = list(zip(dates, values)) |
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return ts |
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market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False) |
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mts = pft.TimeSeries(market_data, "D") |
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print(mts) |
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# print("Datediff=", (mts.end_date - mts.start_date).days) |
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# stock_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False, mu=0.12, sigma=0.15) |
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# sts = pft.TimeSeries(stock_data, "D") |
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# print(sts) |
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# start = time.time() |
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# alpha = pft.jensens_alpha( |
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# asset_data=sts, market_data=mts, risk_free_rate=0.052, return_period_unit="months", return_period_value=1 |
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# ) |
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# print(alpha) |
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# print("Alpha calculation took", time.time() - start, "seconds") |
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# print("Correlation=", pft.correlation(sts, mts)) |
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rr = mts.calculate_rolling_returns(frequency="D") |
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print(117, rr[rr.values < 0.1]) |
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@ -1,100 +0,0 @@ |
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import datetime |
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import math |
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import random |
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import time |
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from typing import List |
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|
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from dateutil.relativedelta import relativedelta |
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|
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import pyfacts as pft |
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|
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|
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def create_prices(s0: float, mu: float, sigma: float, num_prices: int) -> list: |
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"""Generates a price following a geometric brownian motion process based on the input of the arguments. |
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|
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Since this function is used only to generate data for tests, the seed is fixed as 1234. |
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Many of the tests rely on exact values generated using this seed. |
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If the seed is changed, those tests will fail. |
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|
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Parameters: |
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------------ |
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s0: float |
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Asset inital price. |
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|
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mu: float |
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Interest rate expressed annual terms. |
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|
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sigma: float |
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Volatility expressed annual terms. |
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|
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num_prices: int |
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number of prices to generate |
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|
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Returns: |
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-------- |
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Returns a list of values generated using GBM algorithm |
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""" |
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random.seed(1234) # WARNING! Changing the seed will cause most tests to fail |
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all_values = [] |
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for _ in range(num_prices): |
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s0 *= math.exp( |
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(mu - 0.5 * sigma**2) * (1.0 / 365.0) + sigma * math.sqrt(1.0 / 365.0) * random.gauss(mu=0, sigma=1) |
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) |
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all_values.append(round(s0, 2)) |
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return all_values |
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|
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def sample_data_generator( |
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frequency: pft.Frequency, |
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num: int = 1000, |
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skip_weekends: bool = False, |
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mu: float = 0.1, |
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sigma: float = 0.05, |
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eomonth: bool = False, |
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) -> List[tuple]: |
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"""Creates TimeSeries data |
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|
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Parameters: |
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----------- |
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frequency: Frequency |
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The frequency of the time series data to be generated. |
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|
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num: int |
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Number of date: value pairs to be generated. |
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|
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skip_weekends: bool |
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Whether weekends (saturday, sunday) should be skipped. |
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Gets used only if the frequency is daily. |
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|
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mu: float |
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Mean return for the values. |
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sigma: float |
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standard deviation of the values. |
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Returns: |
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-------- |
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Returns a TimeSeries object |
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""" |
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start_date = datetime.datetime(2017, 1, 1) |
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timedelta_dict = { |
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frequency.freq_type: int( |
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frequency.value * num * (7 / 5 if frequency == pft.AllFrequencies.D and skip_weekends else 1) |
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) |
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} |
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end_date = start_date + relativedelta(**timedelta_dict) |
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dates = pft.create_date_series(start_date, end_date, frequency.symbol, skip_weekends=skip_weekends, eomonth=eomonth) |
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values = create_prices(1000, mu, sigma, num) |
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ts = list(zip(dates, values)) |
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return ts |
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market_data = sample_data_generator(num=3600, frequency=pft.AllFrequencies.D, skip_weekends=False) |
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mts = pft.TimeSeries(market_data, "D") |
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print(mts) |
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sortino = pft.sortino_ratio(mts, risk_free_rate=0.05) |
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print(sortino) |
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@ -1,25 +0,0 @@ |
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import datetime |
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from pyfacts.core import Series |
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s1 = Series([2.5, 6.2, 5.6, 8.4, 7.4, 1.5, 9.6, 5]) |
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dt_lst = [ |
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datetime.datetime(2020, 12, 4, 0, 0), |
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datetime.datetime(2019, 5, 16, 0, 0), |
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datetime.datetime(2019, 9, 25, 0, 0), |
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datetime.datetime(2016, 2, 18, 0, 0), |
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datetime.datetime(2017, 8, 14, 0, 0), |
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datetime.datetime(2018, 1, 4, 0, 0), |
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datetime.datetime(2017, 5, 21, 0, 0), |
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datetime.datetime(2018, 7, 17, 0, 0), |
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datetime.datetime(2016, 4, 8, 0, 0), |
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datetime.datetime(2020, 1, 7, 0, 0), |
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datetime.datetime(2016, 12, 24, 0, 0), |
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datetime.datetime(2020, 6, 19, 0, 0), |
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datetime.datetime(2016, 3, 16, 0, 0), |
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datetime.datetime(2017, 4, 25, 0, 0), |
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datetime.datetime(2016, 7, 10, 0, 0), |
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] |
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s2 = Series(dt_lst) |
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