refactor: use Fetcher and add reference tickers
This commit is contained in:
Giulio De Pasquale
parent
600d6b76f6
commit
c11c2bccf4
@ -1,68 +1,74 @@
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from datetime import datetime, timedelta
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from .taapi import TaapiClient
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from typing import List
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from .models import TickerOHLCV
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import yfinance as yf
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import pandas as pd
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class Client:
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def __init__(self, taapi_key: str):
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self._taapi = TaapiClient(taapi_key)
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class Fetcher:
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@staticmethod
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def ticker_data_for(ticker: str, date: datetime) -> TickerOHLCV | None:
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# Set end date to next day to ensure we get the target date
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start_date = date.strftime("%Y-%m-%d")
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end_date = (date + timedelta(days=1)).strftime("%Y-%m-%d")
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def ticker_data_for(
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ticker: str, date: datetime, end_date: datetime | None = None
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) -> List[TickerOHLCV]:
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"""
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Fetch OHLCV data for a ticker for a date range.
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Args:
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ticker: Stock ticker symbol
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date: Start date (inclusive)
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end_date: End date (inclusive). If None, fetches only the start date.
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Returns:
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List of TickerOHLCV records, one per trading day in the range.
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Returns empty list if no data available.
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"""
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start_date_str = date.strftime("%Y-%m-%d")
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if not end_date:
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end_date_str = (date + timedelta(days=1)).strftime("%Y-%m-%d")
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else:
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end_date_str = (end_date + timedelta(days=1)).strftime("%Y-%m-%d")
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try:
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data = yf.download(
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ticker,
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start=start_date,
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end=end_date,
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start=start_date_str,
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end=end_date_str,
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auto_adjust=True,
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progress=False,
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)
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if data.empty:
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return None
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return []
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row = data.iloc[0]
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results: List[TickerOHLCV] = []
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open_price = (
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float(row["Open"].iloc[0])
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if isinstance(row["Open"], pd.Series)
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else float(row["Open"])
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)
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high = (
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float(row["High"].iloc[0])
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if isinstance(row["High"], pd.Series)
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else float(row["High"])
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)
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low = (
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float(row["Low"].iloc[0])
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if isinstance(row["Low"], pd.Series)
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else float(row["Low"])
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)
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close = (
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float(row["Close"].iloc[0])
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if isinstance(row["Close"], pd.Series)
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else float(row["Close"])
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)
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volume = (
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int(row["Volume"].iloc[0])
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if isinstance(row["Volume"], pd.Series)
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else int(row["Volume"])
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if "Volume" in row
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else 0
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)
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for idx, row in data.iterrows():
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# Extract datetime from index
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if hasattr(idx, "to_pydatetime"):
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row_date = idx.to_pydatetime()
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elif isinstance(idx, datetime):
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row_date = idx
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else:
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row_date = pd.to_datetime(idx).to_pydatetime()
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# Extract values (handle both Series and scalar)
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def safe_extract(value):
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if isinstance(value, pd.Series):
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return float(value.iloc[0])
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return float(value)
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open_price = safe_extract(row["Open"])
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high = safe_extract(row["High"])
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low = safe_extract(row["Low"])
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close = safe_extract(row["Close"])
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volume = int(safe_extract(row.get("Volume", 0)))
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# Calculate average price
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avg = (high + low) / 2.0
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return TickerOHLCV(
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ohlcv = TickerOHLCV(
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ticker=ticker,
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date=date,
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date=row_date,
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open=round(open_price, 2),
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high=round(high, 2),
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low=round(low, 2),
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@ -70,8 +76,45 @@ class Client:
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avg=round(avg, 2),
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volume=volume,
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)
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results.append(ohlcv)
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return results
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except Exception as e:
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print(f"Error fetching data for {ticker} on {start_date}: {str(e)}")
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print(
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f"Error fetching data for {ticker} from {start_date_str} to {end_date_str}: {str(e)}"
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)
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return []
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return None
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@staticmethod
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def ticker_data_for_single_day(ticker: str, date: datetime) -> TickerOHLCV | None:
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"""
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Fetch OHLCV data for a single day (backward compatibility).
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Args:
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ticker: Stock ticker symbol
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date: The specific date
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Returns:
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TickerOHLCV record for that day, or None if not available
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"""
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results = Fetcher.ticker_data_for(ticker, date, end_date=None)
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return results[0] if results else None
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@staticmethod
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def ticker_data_for_range(
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ticker: str, start_date: datetime, end_date: datetime
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) -> List[TickerOHLCV]:
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"""
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Fetch OHLCV data for a date range (explicit range method).
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Args:
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ticker: Stock ticker symbol
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start_date: Start date (inclusive)
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end_date: End date (inclusive)
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Returns:
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List of TickerOHLCV records for the range
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"""
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return Fetcher.ticker_data_for(ticker, start_date, end_date)
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@ -50,6 +50,7 @@ class IndicatorService:
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Returns:
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IndicatorsData instance ready to save to database, or None if insufficient data
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or if any calculated values are NaN
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"""
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# Fetch historical OHLCV data from database
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start_date: datetime = target_date - timedelta(
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@ -59,30 +60,40 @@ class IndicatorService:
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ticker=ticker, start_date=start_date, end_date=target_date
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)
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# Verify we have enough data
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if len(ohlcv_records) < 30:
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# Verify we have enough data (minimum 40 trading days for MACD + ADX)
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if len(ohlcv_records) < 40:
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return None
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# Convert to numpy arrays for TA-Lib
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arrays: OHLCVArrays = self._convert_to_arrays(ohlcv_records)
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# Calculate all indicator categories
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momentum: MomentumIndicators = self._calculate_momentum(arrays)
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volatility: VolatilityIndicators = self._calculate_volatility(arrays)
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trend: TrendIndicators = self._calculate_trend(arrays)
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volume: VolumeIndicators = self._calculate_volume(arrays)
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momentum: MomentumIndicators | None = self._calculate_momentum(arrays)
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volatility: VolatilityIndicators | None = self._calculate_volatility(arrays)
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trend: TrendIndicators | None = self._calculate_trend(arrays)
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volume: VolumeIndicators | None = self._calculate_volume(arrays)
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support_resistance: SupportResistanceIndicators = (
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self._calculate_support_resistance(arrays)
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)
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market_regime: MarketRegimeIndicators = self._calculate_market_regime(arrays)
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market_regime: MarketRegimeIndicators | None = self._calculate_market_regime(
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arrays
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)
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# Skip if any indicator group returned None (contains NaN values)
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if (
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momentum is None
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or volatility is None
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or trend is None
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or volume is None
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or market_regime is None
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):
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return None
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# Build and return strongly-typed IndicatorsData
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return IndicatorsData(
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ticker=ticker,
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date=target_date,
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# Momentum (7 indicators)
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rsi_14=momentum.rsi_14,
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rsi_20=momentum.rsi_20,
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macd_line=momentum.macd_line,
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@ -90,40 +101,30 @@ class IndicatorService:
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macd_histogram=momentum.macd_histogram,
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stoch_k=momentum.stoch_k,
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stoch_d=momentum.stoch_d,
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# Volatility (6 indicators)
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bb_upper=volatility.bb_upper,
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bb_middle=volatility.bb_middle,
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bb_lower=volatility.bb_lower,
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bb_width=volatility.bb_width,
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bb_percent=volatility.bb_percent,
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atr_14=volatility.atr_14,
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# Trend (4 indicators)
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adx_14=trend.adx_14,
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di_plus=trend.di_plus,
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di_minus=trend.di_minus,
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sar=trend.sar,
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# Volume (3 indicators)
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obv=volume.obv,
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obv_sma_20=volume.obv_sma_20,
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volume_roc_5=volume.volume_roc_5,
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# Support/Resistance (6 indicators)
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fib_236=support_resistance.fib_236,
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fib_382=support_resistance.fib_382,
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fib_618=support_resistance.fib_618,
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pivot_point=support_resistance.pivot_point,
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resistance_1=support_resistance.resistance_1,
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support_1=support_resistance.support_1,
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# Market Regime (2 indicators)
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cci_20=market_regime.cci_20,
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williams_r_14=market_regime.williams_r_14,
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)
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@ -180,13 +181,14 @@ class IndicatorService:
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) -> int:
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"""
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Calculate and save indicators for multiple dates efficiently.
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Uses upsert logic to handle existing records.
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Args:
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ticker: Stock ticker symbol
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dates: List of dates to calculate indicators for
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Returns:
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Number of indicator records successfully saved
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Number of indicator records successfully saved/updated
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"""
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indicators_list: List[IndicatorsData] = self.bulk_calculate_indicators(
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ticker=ticker, dates=dates
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@ -195,7 +197,14 @@ class IndicatorService:
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if not indicators_list:
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return 0
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return self._crud.create_indicators_bulk(indicators_list)
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# Use upsert for each indicator to avoid UNIQUE constraint violations
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saved_count = 0
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for indicators in indicators_list:
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result = self._crud.upsert_indicators(indicators)
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if result:
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saved_count += 1
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return saved_count
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# ========================================================================
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# PRIVATE: Data Conversion
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48
populate.py
48
populate.py
@ -7,8 +7,7 @@ from paperone.utils import (
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get_last_n_trading_days,
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)
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from paperone.database import TradingDataCRUD
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from os import environ
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from paperone.client import Client
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from paperone.client import Fetcher
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from rich.progress import track
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from datetime import datetime
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@ -16,46 +15,49 @@ load_dotenv()
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DB_FILE = "trading_data.db"
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def main() -> NoReturn:
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api_key = environ.get("API_KEY")
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if not api_key:
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print("API_KEY not set")
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exit(0)
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client = Client(api_key)
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fetcher = Fetcher()
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crud = TradingDataCRUD(f"sqlite:///{DB_FILE}")
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date = datetime.now()
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days_range = 360
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days_range = 360 * 10
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tickers = [
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"AAPL",
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"ARM",
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"^VIX",
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"AMD",
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"GOOG",
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"META",
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"MNDY",
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"MSFT",
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"NVDA",
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"VOO",
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"VT",
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]
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# Loop through tickers and fetch data
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for ticker in tickers:
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reference_tickers = [
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"VOO", # US
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"VT", # global
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"GLD", # gold
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"^VIX", # volatility
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"VNQ", # real estate
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"DBC", # commodities
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"VGK", # EU
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"VPL", # Asia
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"VWO", # Emerging
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"BND", # Global bonds
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]
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for ticker in tickers + reference_tickers:
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trading_days = get_last_n_trading_days(date, days_range)
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for day in track(trading_days, description=f"→ {ticker}"):
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existing = crud.get_ohlcv(ticker, day)
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if existing:
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continue
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start_date = trading_days[0]
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end_date = trading_days[-1]
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ticker_data = client.ticker_data_for(ticker, day)
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if not ticker_data:
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continue
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all_ohlcv_data = fetcher.ticker_data_for(ticker, start_date, end_date)
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for ohlcv in track(all_ohlcv_data, description=f"→ {ticker}"):
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existing = crud.get_ohlcv(ticker, ohlcv.date)
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crud.create_ohlcv(ticker_data)
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if not existing:
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crud.create_ohlcv(ohlcv)
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exit(0)
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