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datasets/stock_higher_frequency/code/StockAggregator.py
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import requests | ||
from dotenv import load_dotenv | ||
import os | ||
import csv | ||
from datetime import datetime, timedelta | ||
import pytz | ||
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class StockAggregator: | ||
def __init__(self, api_key, ticker, api_url="https://api.polygon.io/v2/aggs/ticker", query_limit=50000, timespan="minute", multiplier=1): | ||
self.api_key = api_key | ||
self.base_url = api_url | ||
self.query_limit = query_limit | ||
self.stock_ticker=ticker | ||
self.timespan = timespan | ||
self.multiplier = multiplier | ||
self.dates = [] | ||
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def get_aggregate_bars(self, from_date, to_date, adjusted=True, sort="asc"): | ||
""" | ||
Fetches aggregate bars for a stock over a given date range. | ||
Parameters: | ||
from_date (str): The start date in "YYYY-MM-DD" format. | ||
to_date (str): The end date in "YYYY-MM-DD" format. | ||
adjusted (bool): Whether or not the results are adjusted for splits. | ||
sort (str): Sort order of the results ("asc" or "desc"). | ||
Returns: | ||
dict: The API response containing aggregate bars data. | ||
""" | ||
print(f'From: {from_date}\tto: {to_date}') | ||
# Construct the API request URL | ||
request_url = f"{self.base_url}/{self.stock_ticker}/range/{self.multiplier}/{self.timespan}/{from_date}/{to_date}?adjusted={str(adjusted).lower()}&sort={sort}&limit={self.query_limit}&apiKey={self.api_key}" | ||
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# Make the API request | ||
response = requests.get(request_url, timeout=30) | ||
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# Check if the request was successful | ||
if response.status_code == 200: | ||
return response.json() | ||
else: | ||
print(f"Failed to fetch data: {response.status_code}. Error Message: ") | ||
print(f"\t => {response.text}") | ||
exit | ||
return None | ||
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# def __to_csv(self, data, filename): | ||
# """ | ||
# Saves the fetched data to a CSV file. | ||
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# Parameters: | ||
# data (dict): The data returned by the API. | ||
# filename (str): The filename of the CSV file to save the data. | ||
# """ | ||
# if not data or 'results' not in data: | ||
# print("No data to save.") | ||
# return | ||
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# # Assuming 'results' contains the list of data points to be saved | ||
# results = data['results'] | ||
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# # Open the file and prepare to write | ||
# with open(filename, 'w', newline='') as file: | ||
# # Determine the fieldnames from the first item's keys | ||
# fieldnames = results[0].keys() | ||
# writer = csv.DictWriter(file, fieldnames=fieldnames) | ||
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# # Write the header and the rows | ||
# writer.writeheader() | ||
# for row in results: | ||
# writer.writerow(row) | ||
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# print(f"Data successfully saved to {filename}.") | ||
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def __to_csv(self, data, filename): | ||
if not data or 'results' not in data: | ||
print("No data to save.") | ||
return | ||
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results = data['results'] | ||
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with open(filename, 'w', newline='') as file: | ||
fieldnames = list(results[0].keys()) + ['datetime_est'] | ||
writer = csv.DictWriter(file, fieldnames=fieldnames) | ||
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writer.writeheader() | ||
for row in results: | ||
row['datetime_est'] = self.__convert_timestamp_to_est(row['t']) | ||
writer.writerow(row) | ||
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print(f"Data successfully saved to {filename}.") | ||
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# def __append_to_csv(self, data, filename): | ||
# """ | ||
# Appends the fetched data to a CSV file. If the file doesn't exist, it creates it. | ||
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# Parameters: | ||
# data (dict): The data returned by the API. | ||
# filename (str): The filename of the CSV file to append the data. | ||
# """ | ||
# if not data or 'results' not in data: | ||
# print("No data to append.") | ||
# return | ||
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# should_write_header = not os.path.exists(filename) or os.path.getsize(filename) == 0 | ||
# results = data['results'] | ||
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# with open(filename, 'a', newline='') as file: | ||
# fieldnames = results[0].keys() | ||
# writer = csv.DictWriter(file, fieldnames=fieldnames) | ||
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# if should_write_header: | ||
# writer.writeheader() | ||
# for row in results: | ||
# writer.writerow(row) | ||
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# print(f"Data successfully appended to {filename}.") | ||
def __append_to_csv(self, data, filename): | ||
if not data or 'results' not in data: | ||
print("No data to append.") | ||
return | ||
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should_write_header = not os.path.exists(filename) or os.path.getsize(filename) == 0 | ||
results = data['results'] | ||
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with open(filename, 'a', newline='') as file: | ||
fieldnames = list(results[0].keys()) + ['datetime_est'] | ||
writer = csv.DictWriter(file, fieldnames=fieldnames) | ||
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if should_write_header: | ||
writer.writeheader() | ||
for row in results: | ||
row['datetime_est'] = self.__convert_timestamp_to_est(row['t']) | ||
writer.writerow(row) | ||
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print(f"Data successfully appended to {filename}.") | ||
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def fetch_and_save_data(self, from_date, to_date, interval_days, filename): | ||
""" | ||
Fetches data at specified interval days from from_date to to_date and saves it to a CSV file. | ||
""" | ||
start_date = datetime.strptime(from_date, "%Y-%m-%d") | ||
end_date = datetime.strptime(to_date, "%Y-%m-%d") | ||
current_date = start_date | ||
first_fetch = True | ||
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while current_date <= end_date: | ||
# Calculate the next date based on the interval | ||
next_date = current_date + timedelta(days=interval_days) | ||
# Adjust the next_date not to exceed the to_date | ||
if next_date > end_date: | ||
next_date = end_date + timedelta(days=1) # to include the end_date in the range | ||
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# Fetch data for the current interval | ||
data = self.get_aggregate_bars(from_date=current_date.strftime("%Y-%m-%d"), | ||
to_date=next_date.strftime("%Y-%m-%d")) | ||
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if data and data['results']: | ||
if first_fetch: | ||
self.__to_csv(data, filename) | ||
first_fetch = False | ||
else: | ||
self.__append_to_csv(data, filename) | ||
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current_date = next_date | ||
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def __convert_timestamp_to_est(self, timestamp): | ||
""" | ||
Converts a UNIX timestamp (in milliseconds) to a datetime object in EST. | ||
""" | ||
utc_time = datetime.utcfromtimestamp(timestamp / 1000.0) | ||
utc_time = utc_time.replace(tzinfo=pytz.utc) | ||
est_time = utc_time.astimezone(pytz.timezone('US/Eastern')) | ||
return est_time.strftime('%Y-%m-%d %H:%M:%S') | ||
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def generate_interval_dates (self, start_date_str, interval_days_int, end_date_str=None, print_dates=False): | ||
# Define the start date and interval | ||
start_date = datetime.strptime(start_date_str, "%Y-%m-%d") | ||
interval = timedelta(days=interval_days_int) | ||
if end_date_str is None: | ||
end_date = datetime.now() | ||
else: | ||
end_date = datetime.strptime(end_date_str, "%Y-%m-%d") | ||
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# Generate dates from start_date + interval days until today | ||
self.dates = [] | ||
current_date = start_date | ||
while current_date <= end_date: | ||
self.dates.append(current_date) | ||
current_date += interval | ||
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if (print_dates): | ||
# Convert dates to string format for display | ||
dates_str = [date.strftime("%Y-%m-%d") for date in self.dates] | ||
print("Dates generated: ") | ||
for date_str in dates_str: | ||
print(f"\t => {date_str}") | ||
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def main(): | ||
# Load environment variables from .env file | ||
load_dotenv('code/.env') | ||
api_key = os.getenv("API_KEY") | ||
print("API_KEY : ", api_key) | ||
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for ticker in ['UNH', 'MSFT', 'GS', 'HD', 'CAT']: | ||
stock_aggregator = StockAggregator(api_key=api_key, ticker=ticker) | ||
stock_aggregator.fetch_and_save_data(from_date="2014-01-01", | ||
to_date="2024-01-01", | ||
interval_days=3, | ||
filename=f"data/{ticker}.csv") | ||
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main() |
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API_KEY=test |