Whole Code : -
from datetime import datetime, timedelta
def get_start_end_date(daylength = 95):
# Get today's date
today = datetime.today()
# Get the date 75 days ago
previous_date = today - timedelta(days=daylength) # 95
# Get the date 1 day ago
# yesterday = today - timedelta(days=1)
# # Format the date
# yesterday_str = yesterday.strftime('%Y-%m-%d')
# Format the dates
today_str = today.strftime('%Y-%m-%d')
previous_date_str = previous_date.strftime('%Y-%m-%d')
start_date = previous_date_str
end_date = today_str
return start_date, end_date
import threading
import time
from twelvedata import TDClient
# Function to simulate processing of a forex pair
def process_forex_pair(symbol, tf, start_date, end_date, output_size, td ):
print(f"Processing {symbol}...")
if tf == None : tf = "4h"
symbol_ = symbol[:3]+"/"+symbol[:-3]
ts = td.time_series(
symbol= symbol_, # "EUR/USD", # symbol_
interval=tf , #"4h", # tf
start_date=f"{start_date}",
end_date=f"{end_date}",
outputsize=output_size ) # 450 ) # approximate length for
# Returns pandas.DataFrame
df = ts.as_pandas()
folder_path = "currency_data"
if not os.path.exists(folder_path):
os.makedirs(folder_path)
filename = f"{symbol}.csv"
if tf == "1day":
filename = f"{symbol}_Daily.csv"
elif tf == "4h":
filename = f"{symbol}_H4.csv"
file_path = os.path.join(folder_path, filename)
df.to_csv(f"{file_path}")
# Simulate some work with a sleep (replace with actual processing logic)
time.sleep(0.5)
print(f"Completed processing {symbol}")
# Main function to manage threading
def main():
td = TDClient(apikey="")
forex_pairs = [
'AUDCAD', 'AUDCHF', 'AUDJPY', 'AUDNZD', 'AUDUSD',
'CADCHF', 'CADJPY',
'CHFJPY',
'EURAUD', 'EURCAD', 'EURCHF', 'EURGBP',
'EURJPY', 'EURNZD', 'EURUSD',
'GBPAUD', 'GBPCAD', 'GBPCHF',
'GBPJPY', 'GBPUSD', 'GBPNZD',
'NZDCAD', 'NZDCHF', 'NZDJPY', 'NZDUSD',
'USDCHF', 'USDCAD', 'USDJPY'
]
start_date, end_date = get_start_end_date()
daylength = 95
tf = "1day"
if tf == "1day":
outputsize = daylength # last 95 days
elif tf == "4h":
outputsize = daylength * 6 # 95 days length
batch_size = 8 # limit of requests api can make # Number of threads to run simultaneously
output_size = daylength
for i in range(0, len(forex_pairs), batch_size):
# Create a batch of threads
# threads = []
for symbol in forex_pairs[i:i + batch_size]:
process_forex_pair(symbol, tf, start_date, end_date, output_size, td)
# Wait for 60 seconds before moving on to the next batch
print("Waiting for 60 seconds before processing the next batch...")
time.sleep(60)
print("All forex pairs have been processed.")
# Run the main function
if __name__ == "__main__":
main()
from datetime import datetime, timedelta
# Get today's date
today = datetime.today()
# Get the date 75 days ago
previous_date = today - timedelta(days=95)
# Get the date 1 day ago
yesterday = today - timedelta(days=1)
# Format the date
yesterday_str = yesterday.strftime('%Y-%m-%d')
# Format the dates
today_str = today.strftime('%Y-%m-%d')
previous_date_str = previous_date.strftime('%Y-%m-%d')
print("Today's Date:", today_str)
print("75 Days Ago Date:", previous_date_str)
from twelvedata import TDClient
# Initialize client - apikey parameter is requiered
td = TDClient(apikey="")
tf = None
tf = "1day" # "1day"
if tf == "1day":
outputsize = 95 # last 95 days
elif tf == "4h":
outputsize = 95 * 6 # 95 days length
# Construct the necessary time series
ts = td.time_series(
symbol="EUR/USD" , # , GBP/USD
interval=tf, # "4h"
start_date=f"{previous_date_str}",
end_date= f"{today_str}",
outputsize=outputsize # 450 # approximate length for
)
df = ts.as_pandas()
print(ts.as_pandas())
df.to_csv("eurusd_daily.csv")
show us all OHLC from start date to yesterday only excluding today date when
start_date=f"{previous_date_str}", end_date= f"{today_str}", are used. Length is 69 index of Day
20 May 2024 To 22 August 2024 excluding today's date 23 August
print(len(df))
df.to_csv("eurusd_hour_4.csv")
In 4 Hour Timeframe show us all OHLC from start date to yesterday only excluding today date when
start_date=f"{previous_date_str}", end_date= f"{today_str}", are used. Length is 413 index of hour-4
20 May 2024 To 22 August 2024 excluding today's date 23 August
Append Function To take new dataframe and dataframe file name and then append new records after previous records,
import pandas as pd
def append_new_data(csv_file_path, new_data_df):
"""
Appends new data to the existing CSV file without overwriting existing records.
Parameters:
csv_file_path (str): The path to the CSV file.
new_data_df (pd.DataFrame): The new data to append (must include a 'date' column).
Returns:
None
"""
# Step 1: Read the existing data from the CSV file
try:
existing_df = pd.read_csv(csv_file_path, parse_dates=["date"])
except FileNotFoundError:
existing_df = pd.DataFrame() # Create an empty DataFrame if the file doesn't exist
# Step 2: Ensure the date column in the new data is in datetime format
new_data_df['date'] = pd.to_datetime(new_data_df['date'])
# Step 3: Filter out data from `new_data_df` that is already in `existing_df`
if not existing_df.empty:
filtered_new_data = new_data_df[~new_data_df['date'].isin(existing_df['date'])]
else:
filtered_new_data = new_data_df # If the existing_df is empty, all data is new
# Step 4: Append the new data to the existing data
combined_df = pd.concat([existing_df, filtered_new_data])
# Step 5: Save the combined data back to the CSV file
combined_df.to_csv(csv_file_path, index=False)
print(f"Data successfully appended to {csv_file_path}")
# Example usage:
# Assuming `df` is the new data DataFrame with a 'date' column
# append_new_data("eurusd_daily.csv", df)
from datetime import datetime, timedelta
# Get today's date
today = datetime.today()
# Get the date 75 days ago
previous_date = today - timedelta(days=95)
# Get the date 1 day ago
yesterday = today - timedelta(days=1)
# Format the date
yesterday_str = yesterday.strftime('%Y-%m-%d')
# Format the dates
today_str = today.strftime('%Y-%m-%d')
previous_date_str = previous_date.strftime('%Y-%m-%d')
print("Today's Date:", today_str)
print("75 Days Ago Date:", previous_date_str)
from twelvedata import TDClient
api_key = "_"
# Initialize client - apikey parameter is requiered
td = TDClient(apikey=api_key)
# Construct the necessary time series
ts = td.time_series(
symbol="EUR/USD",
interval="4h",
start_date=f"{previous_date_str}",
end_date= f"{today_str}",
outputsize=450
)
# Returns pandas.DataFrame
print(ts.as_pandas())