Krikorios
48e60d015f
- Add advanced metrics dashboard with trade analytics - Add new trading components (EntryTypeAnalysis, MultiDayPositionTracker, NewsEventTracker, etc.) - Add strategy mode selector and trend confirmation - Add risk automation panel and slippage correlation analysis - Add daily trading plan enhancements with modal components - Add custom hooks (useApi, useLocalStorage, useAdvancedTradeMetrics) - Add broker service integration and trading API - Add test setup and vitest configuration - Include parquet data files for live market data - Add comprehensive documentation in docs/ folder
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
from __future__ import annotations
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import asyncio
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from typing import List, Optional
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import pandas as pd
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import yfinance as yf
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from app.schemas.schemas import PriceData
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YA_SYMBOL = "XAUUSD=X"
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def _format_dataframe(df: pd.DataFrame) -> List[PriceData]:
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rows: List[PriceData] = []
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if df.empty:
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return rows
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df = df.dropna(subset=["Open", "High", "Low", "Close"])
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for idx, row in df.iterrows():
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timestamp = int(pd.Timestamp(idx).timestamp())
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rows.append(
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PriceData(
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time=timestamp,
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open=float(row["Open"]),
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high=float(row["High"]),
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low=float(row["Low"]),
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close=float(row["Close"]),
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volume=float(row.get("Volume", 0.0) or 0.0),
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)
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)
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return rows
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async def fetch_yfinance_history(
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symbol: str = YA_SYMBOL,
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interval: str = "1m",
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period: str = "1d",
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start: Optional[str] = None,
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end: Optional[str] = None,
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) -> List[PriceData]:
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def _download() -> pd.DataFrame:
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return yf.download(
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symbol,
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interval=interval,
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period=None if start else period,
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start=start,
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end=end,
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progress=False,
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auto_adjust=False,
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threads=False,
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)
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df = await asyncio.to_thread(_download)
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return _format_dataframe(df)
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async def fetch_yfinance_quote(symbol: str = YA_SYMBOL) -> Optional[dict]:
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rows = await fetch_yfinance_history(symbol=symbol, interval="1m", period="1d")
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if not rows:
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return None
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latest = rows[-1]
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previous = rows[-2] if len(rows) > 1 else latest
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return {
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"price": latest.close,
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"previous_close": previous.close,
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"high_24h": max(r.high for r in rows[-1440:]),
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"low_24h": min(r.low for r in rows[-1440:]),
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"volume": latest.volume or 0.0,
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"updated_at": latest.time,
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"rows": rows,
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}
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