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a1bf219 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | """Abstract base class for data providers."""
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, Dict, List, Literal, Optional
import pandas as pd
class ProviderException(Exception):
"""Custom exception for provider errors."""
pass
AssetType = Literal["stock", "crypto", "commodity", "index", "forex", "unknown"]
class DataProvider(ABC):
"""Abstract base class for market data providers."""
@abstractmethod
def fetch_ohlc(
self, ticker: str, timeframe: str, start_date: str, end_date: str
) -> pd.DataFrame:
"""
Fetch OHLC price data.
Args:
ticker: Asset ticker symbol (e.g., "AAPL", "BTC-USD")
timeframe: Candlestick timeframe ("1m", "5m", "15m", "30m", "1h", "4h", "1d")
start_date: Start date in YYYY-MM-DD format
end_date: End date in YYYY-MM-DD format
Returns:
DataFrame with columns: timestamp, open, high, low, close, volume
Raises:
ProviderException: If data fetch fails
"""
pass
@abstractmethod
def fetch_fundamentals(self, ticker: str) -> Dict[str, Any]:
"""
Fetch fundamental data (financials, earnings).
Args:
ticker: Stock ticker symbol
Returns:
Dictionary with fundamental data (market_cap, pe_ratio, revenue, etc.)
Raises:
ProviderException: If data fetch fails
NotImplementedError: If provider doesn't support fundamentals
"""
pass
@abstractmethod
def fetch_news(self, ticker: str, limit: int = 10) -> List[Dict[str, Any]]:
"""
Fetch recent news articles.
Args:
ticker: Asset ticker symbol
limit: Maximum number of articles to return
Returns:
List of news articles with title, source, published_at, summary
Raises:
ProviderException: If data fetch fails
"""
pass
@abstractmethod
def is_available(self) -> bool:
"""
Check if provider is reachable.
Returns:
True if provider is available, False otherwise
"""
pass
def _validate_ohlc(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Validate OHLC data integrity.
Args:
df: DataFrame with OHLC data
Returns:
Validated DataFrame
Raises:
ProviderException: If validation fails
"""
if df.empty:
raise ProviderException("No data returned from provider")
required_columns = ["open", "high", "low", "close", "volume"]
missing_columns = [col for col in required_columns if col not in df.columns]
if missing_columns:
raise ProviderException(f"Missing required columns: {missing_columns}")
# Drop rows with NaN values in critical columns
df = df.dropna(subset=["open", "high", "low", "close"])
if df.empty:
raise ProviderException("No valid data after removing NaN values")
# Validate OHLC relationships
invalid_rows = (
(df["low"] > df["open"])
| (df["low"] > df["close"])
| (df["high"] < df["open"])
| (df["high"] < df["close"])
| (df["high"] < df["low"])
)
if invalid_rows.any():
raise ProviderException(
f"Invalid OHLC relationships in {invalid_rows.sum()} rows"
)
return df
@staticmethod
def detect_asset_type(ticker: str) -> AssetType:
"""
Detect asset type based on ticker format.
Args:
ticker: Asset ticker symbol
Returns:
Asset type: "stock", "crypto", "commodity", "index", "forex", or "unknown"
Examples:
- "AAPL" -> "stock"
- "BTC-USD" -> "crypto"
- "ETH-USD" -> "crypto"
- "GC=F" -> "commodity" (Gold futures)
- "CL=F" -> "commodity" (Crude oil futures)
- "^GSPC" -> "index" (S&P 500)
- "^DJI" -> "index" (Dow Jones)
- "EURUSD=X" -> "forex"
"""
ticker_upper = ticker.upper()
# Index detection (starts with ^)
if ticker_upper.startswith("^"):
return "index"
# Commodity futures detection (ends with =F)
if ticker_upper.endswith("=F"):
return "commodity"
# Forex detection (ends with =X or common forex pairs)
if ticker_upper.endswith("=X"):
return "forex"
# Crypto detection (contains -USD, -USDT, -USDC, etc.)
crypto_suffixes = ["-USD", "-USDT", "-USDC", "-BUSD", "-EUR", "-GBP"]
if any(ticker_upper.endswith(suffix) for suffix in crypto_suffixes):
return "crypto"
# Common crypto prefixes
crypto_prefixes = ["BTC", "ETH", "BNB", "ADA", "SOL", "XRP", "DOGE", "MATIC"]
ticker_prefix = ticker_upper.split("-")[0]
if ticker_prefix in crypto_prefixes:
return "crypto"
# Default to stock for standard ticker symbols
# Most stocks are 1-5 uppercase letters without special characters
if ticker_upper.replace(".", "").isalpha() and len(ticker_upper) <= 5:
return "stock"
# Unknown for anything else
return "unknown"
@staticmethod
def get_asset_characteristics(asset_type: AssetType) -> Dict[str, Any]:
"""
Get characteristics and constraints for different asset types.
Args:
asset_type: Type of asset
Returns:
Dictionary with asset characteristics including:
- market_hours: Trading hours (24/7 vs market hours)
- has_fundamentals: Whether fundamental analysis applies
- volatility: Typical volatility level
- analysis_focus: Primary analysis considerations
"""
characteristics = {
"stock": {
"market_hours": "9:30 AM - 4:00 PM ET (Mon-Fri)",
"has_fundamentals": True,
"volatility": "moderate",
"analysis_focus": [
"Company financials",
"Earnings reports",
"Technical patterns",
"Sector trends",
],
"fundamental_factors": [
"revenue_growth",
"earnings_per_share",
"pe_ratio",
"debt_to_equity",
],
},
"crypto": {
"market_hours": "24/7 (365 days)",
"has_fundamentals": False,
"volatility": "high",
"analysis_focus": [
"Market sentiment",
"Technical patterns",
"Trading volume",
"News and social media",
],
"fundamental_factors": [
"market_sentiment",
"adoption_rate",
"regulatory_news",
"whale_activity",
],
},
"commodity": {
"market_hours": "Various (depends on commodity)",
"has_fundamentals": False,
"volatility": "moderate-high",
"analysis_focus": [
"Supply and demand",
"Geopolitical events",
"Seasonal patterns",
"Technical levels",
],
"fundamental_factors": [
"supply_demand_balance",
"inventory_levels",
"geopolitical_risk",
"weather_patterns",
],
},
"index": {
"market_hours": "9:30 AM - 4:00 PM ET (Mon-Fri)",
"has_fundamentals": False,
"volatility": "moderate",
"analysis_focus": [
"Sector rotation",
"Macro sentiment",
"Technical patterns",
"Breadth indicators",
],
"fundamental_factors": [
"sector_performance",
"market_breadth",
"economic_indicators",
"constituent_earnings",
],
},
"forex": {
"market_hours": "24/5 (Sun 5 PM - Fri 5 PM ET)",
"has_fundamentals": False,
"volatility": "moderate",
"analysis_focus": [
"Interest rate differentials",
"Economic data",
"Central bank policy",
"Technical levels",
],
"fundamental_factors": [
"interest_rates",
"gdp_growth",
"inflation",
"central_bank_policy",
],
},
"unknown": {
"market_hours": "unknown",
"has_fundamentals": False,
"volatility": "unknown",
"analysis_focus": ["Technical patterns", "Price action"],
"fundamental_factors": [],
},
}
return characteristics.get(asset_type, characteristics["unknown"])
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