Create data.py

data.py

@@ -0,0 +1,115 @@
+# -*- coding: utf-8 -*-
+# Módulo para descarga y preparación de datos desde APIs y web
+
+import pandas as pd
+import numpy as np
+import requests
+import time
+import yfinance as yf
+from datetime import datetime, timedelta
+
+from config import CRYPTOCOMPARE_API_KEY, CRYPTOCOMPARE_BASE_URL
+from utils import limpiar_texto, expand_money, descargar_tabla_web
+
+# Diccionario global para almacenar datos históricos
+all_historical_data = {}
+
+def fetch_btc_data_cryptocompare():
+    """Descarga datos horarios de BTC desde CryptoCompare para los últimos 10 años."""
+    SYMBOL = "BTC"
+    CURRENCY = "USD"
+    LIMIT_PER_CALL = 2000
+
+    end_time = datetime.now()
+    start_time = end_time - timedelta(days=10 * 365)
+    to_timestamp = int(end_time.timestamp())
+    all_data = []
+
+    while True:
+        params = {
+            "fsym": SYMBOL,
+            "tsym": CURRENCY,
+            "limit": LIMIT_PER_CALL,
+            "toTs": to_timestamp,
+            "api_key": CRYPTOCOMPARE_API_KEY
+        }
+        try:
+            response = requests.get(CRYPTOCOMPARE_BASE_URL, params=params)
+            data = response.json()
+
+            if data['Response'] == 'Error':
+                print(f"API Error: {data['Message']}")
+                break
+
+            ohlcv_data = data['Data']['Data']
+            if not ohlcv_data:
+                break
+
+            all_data.extend(ohlcv_data)
+            to_timestamp = ohlcv_data[0]['time'] - 1
+            oldest_data_time = datetime.fromtimestamp(ohlcv_data[0]['time'])
+
+            if oldest_data_time <= start_time:
+                break
+
+            time.sleep(1)
+        except requests.exceptions.RequestException as e:
+            print(f"Network error: {e}")
+            time.sleep(5)
+        except Exception as e:
+            print(f"Unexpected error: {e}")
+            break
+
+    btc_df = pd.DataFrame(all_data)
+    btc_df['time'] = pd.to_datetime(btc_df['time'], unit='s')
+    btc_df.set_index('time', inplace=True)
+    btc_df.sort_index(inplace=True)
+    btc_df = btc_df[~btc_df.index.duplicated(keep='first')]
+    btc_df.index = btc_df.index.tz_localize('UTC').tz_convert('America/New_York')
+    btc_df = btc_df[btc_df.index.hour == 16]
+    btc_df.index = btc_df.index.date
+    btc_df.columns = btc_df.columns.str.title()
+    btc_df.index = pd.to_datetime(btc_df.index)
+
+    all_historical_data['BTC-USD'] = btc_df
+    return btc_df
+
+def generar_tickers():
+    """Genera una lista global de tickers a partir de las tablas de stocks y ETFs."""
+    tickers = []
+    url_stocks = "https://bitcointreasuries.net/"
+    url_etfs = "https://bitcointreasuries.net/etfs-and-exchanges"
+
+    df_stocks = descargar_tabla_web(url_stocks, indice_tabla=3)
+    df_etfs = descargar_tabla_web(url_etfs, indice_tabla=3)
+
+    if df_stocks is not None and not df_stocks.empty:
+        df_stocks['Name'] = df_stocks[2].apply(limpiar_texto)
+        tickers.extend(df_stocks['Name'].dropna().tolist())
+
+    if df_etfs is not None and not df_etfs.empty:
+        df_etfs['Name'] = df_etfs[2].apply(limpiar_texto)
+        tickers.extend(df_etfs['Name'].dropna().tolist())
+
+    return tickers
+
+def data_option(ticker, date, option_type, size_of_the_window=4):
+    """Obtiene datos de opciones para un ticker en una fecha específica."""
+    price = yf.Ticker(ticker).info['regularMarketPrice']
+    chain = yf.Ticker(ticker).option_chain(date)
+    data = chain.puts if option_type == 'put' else chain.calls
+    data = data.iloc[:, [0, 2, 3, 8, 10, 11, 13]]
+    data['impliedVolatility'] = data['impliedVolatility'].apply(lambda x: f"{x*100:.2f}%")
+    data = data.sort_values(by='strike', key=lambda x: abs(x - price))
+
+    price_row = pd.DataFrame([[
+        '░░░░░░░ Spot Price ░░░░░░░', price, '░'*13, '░'*10, '░'*18, '░'*11, '░'*10
+    ]], columns=data.columns)
+
+    date_row = pd.DataFrame([[
+        f"▓▓▓▓▓▓▓ {date} ▓▓▓▓▓▓▓", '▓'*7, '▓'*13, '▓'*10, '▓'*18, '▓'*11, '▓'*10
+    ]], columns=data.columns)
+
+    data = pd.concat([data.head(size_of_the_window), price_row]).sort_values(by='strike')
+    data = pd.concat([date_row, data])
+    return data.reset_index(drop=True)