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TradingAgents/tradingagents/dataflows/optimized_us_data.py.diff

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# 文件差异报告
# 当前文件: tradingagents\dataflows\optimized_us_data.py
# 中文版文件: TradingAgentsCN\tradingagents\dataflows\optimized_us_data.py
# 生成时间: 周日 2025/07/06
--- current/optimized_us_data.py+++ chinese_version/optimized_us_data.py@@ -1,7 +1,7 @@ #!/usr/bin/env python3
"""
-Optimized US Stock Data Fetcher
-Integrates caching strategy to reduce API calls and improve response speed
+优化的美股数据获取工具
+集成缓存策略，减少API调用，提高响应速度
"""
import os
@@ -16,24 +16,24 @@
class OptimizedUSDataProvider:
- """Optimized US Stock Data Provider - Integrates caching and API rate limiting"""
+ """优化的美股数据提供器 - 集成缓存和API限制处理"""
def __init__(self):
self.cache = get_cache()
self.config = get_config()
self.last_api_call = 0
- self.min_api_interval = 1.0 # Minimum API call interval (seconds)
-
- print("📊 Optimized US stock data provider initialized")
+ self.min_api_interval = 1.0 # 最小API调用间隔（秒）
+
+ print("📊 优化美股数据提供器初始化完成")
def _wait_for_rate_limit(self):
- """Wait for API rate limit"""
+ """等待API限制"""
current_time = time.time()
time_since_last_call = current_time - self.last_api_call
if time_since_last_call < self.min_api_interval:
wait_time = self.min_api_interval - time_since_last_call
- print(f"⏳ API rate limit wait {wait_time:.1f}s...")
+ print(f"⏳ API限制等待 {wait_time:.1f}s...")
time.sleep(wait_time)
self.last_api_call = time.time()
@@ -41,364 +41,292 @@ def get_stock_data(self, symbol: str, start_date: str, end_date: str,
force_refresh: bool = False) -> str:
"""
- Get US stock data - prioritize cache usage
+ 获取美股数据 - 优先使用缓存
Args:
- symbol: Stock symbol
- start_date: Start date (YYYY-MM-DD)
- end_date: End date (YYYY-MM-DD)
- force_refresh: Whether to force refresh cache
-
+ symbol: 股票代码
+ start_date: 开始日期 (YYYY-MM-DD)
+ end_date: 结束日期 (YYYY-MM-DD)
+ force_refresh: 是否强制刷新缓存
+
Returns:
- Formatted stock data string
+ 格式化的股票数据字符串
"""
+ print(f"📈 获取美股数据: {symbol} ({start_date} 到 {end_date})")
+
+ # 检查缓存（除非强制刷新）
+ if not force_refresh:
+ # 优先查找FINNHUB缓存
+ cache_key = self.cache.find_cached_stock_data(
+ symbol=symbol,
+ start_date=start_date,
+ end_date=end_date,
+ data_source="finnhub"
+ )
+
+ # 如果没有FINNHUB缓存，查找Yahoo Finance缓存
+ if not cache_key:
+ cache_key = self.cache.find_cached_stock_data(
+ symbol=symbol,
+ start_date=start_date,
+ end_date=end_date,
+ data_source="yfinance"
+ )
+
+ if cache_key:
+ cached_data = self.cache.load_stock_data(cache_key)
+ if cached_data:
+ print(f"⚡ 从缓存加载美股数据: {symbol}")
+ return cached_data
+
+ # 缓存未命中，从API获取 - 优先使用FINNHUB
+ formatted_data = None
+ data_source = None
+
+ # 尝试FINNHUB API（优先）
try:
- # Check cache first (unless force refresh)
- if not force_refresh:
- cache_key = self.cache.find_cached_stock_data(
- symbol, start_date, end_date, "optimized_yfinance"
- )
-
- if cache_key and self.cache.is_cache_valid(cache_key, symbol):
- cached_data = self.cache.load_stock_data(cache_key)
- if cached_data:
- print(f"📖 Using cached data for {symbol}")
- if isinstance(cached_data, pd.DataFrame):
- return self._format_stock_data(cached_data, symbol)
- else:
- return cached_data
-
- # Fetch new data from API
- print(f"🌐 Fetching new data for {symbol} from {start_date} to {end_date}")
-
- # Wait for rate limit
+ print(f"🌐 从FINNHUB API获取数据: {symbol}")
self._wait_for_rate_limit()
-
- # Try Yahoo Finance first
+
+ formatted_data = self._get_data_from_finnhub(symbol, start_date, end_date)
+ if formatted_data and "❌" not in formatted_data:
+ data_source = "finnhub"
+ print(f"✅ FINNHUB数据获取成功: {symbol}")
+ else:
+ print(f"⚠️ FINNHUB数据获取失败，尝试备用方案")
+ formatted_data = None
+
+ except Exception as e:
+ print(f"❌ FINNHUB API调用失败: {e}")
+ formatted_data = None
+
+ # 备用方案：Yahoo Finance API
+ if not formatted_data:
try:
- data = self._fetch_from_yfinance(symbol, start_date, end_date)
- if data is not None and not data.empty:
- # Cache the DataFrame
- cache_key = self.cache.save_stock_data(
- symbol, data, start_date, end_date, "optimized_yfinance"
- )
+ print(f"🌐 从Yahoo Finance API获取数据: {symbol}")
+ self._wait_for_rate_limit()
+
+ # 获取数据
+ ticker = yf.Ticker(symbol.upper())
+ data = ticker.history(start=start_date, end=end_date)
+
+ if data.empty:
+ error_msg = f"未找到股票 '{symbol}' 在 {start_date} 到 {end_date} 期间的数据"
+ print(f"❌ {error_msg}")
+ else:
+ # 格式化数据
+ formatted_data = self._format_stock_data(symbol, data, start_date, end_date)
+ data_source = "yfinance"
+ print(f"✅ Yahoo Finance数据获取成功: {symbol}")
+
+ except Exception as e:
+ print(f"❌ Yahoo Finance API调用失败: {e}")
+ formatted_data = None
+
+ # 如果所有API都失败，生成备用数据
+ if not formatted_data:
+ error_msg = "所有美股数据源都不可用"
+ print(f"❌ {error_msg}")
+ return self._generate_fallback_data(symbol, start_date, end_date, error_msg)
+
+ # 保存到缓存
+ self.cache.save_stock_data(
+ symbol=symbol,
+ data=formatted_data,
+ start_date=start_date,
+ end_date=end_date,
+ data_source=data_source
+ )
+
+ return formatted_data
+
+ def _format_stock_data(self, symbol: str, data: pd.DataFrame,
+ start_date: str, end_date: str) -> str:
+ """格式化股票数据为字符串"""
+
+ # 移除时区信息
+ if data.index.tz is not None:
+ data.index = data.index.tz_localize(None)
+
+ # 四舍五入数值
+ numeric_columns = ["Open", "High", "Low", "Close", "Adj Close"]
+ for col in numeric_columns:
+ if col in data.columns:
+ data[col] = data[col].round(2)
+
+ # 获取最新价格和统计信息
+ latest_price = data['Close'].iloc[-1]
+ price_change = data['Close'].iloc[-1] - data['Close'].iloc[0]
+ price_change_pct = (price_change / data['Close'].iloc[0]) * 100
+
+ # 计算技术指标
+ data['MA5'] = data['Close'].rolling(window=5).mean()
+ data['MA10'] = data['Close'].rolling(window=10).mean()
+ data['MA20'] = data['Close'].rolling(window=20).mean()
+
+ # 计算RSI
+ delta = data['Close'].diff()
+ gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+ loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+ rs = gain / loss
+ rsi = 100 - (100 / (1 + rs))
+
+ # 格式化输出
+ result = f"""# {symbol} 美股数据分析
+
+## 📊 基本信息
+- 股票代码: {symbol}
+- 数据期间: {start_date} 至 {end_date}
+- 数据条数: {len(data)}条
+- 最新价格: ${latest_price:.2f}
+- 期间涨跌: ${price_change:+.2f} ({price_change_pct:+.2f}%)
+
+## 📈 价格统计
+- 期间最高: ${data['High'].max():.2f}
+- 期间最低: ${data['Low'].min():.2f}
+- 平均成交量: {data['Volume'].mean():,.0f}
+
+## 🔍 技术指标
+- MA5: ${data['MA5'].iloc[-1]:.2f}
+- MA10: ${data['MA10'].iloc[-1]:.2f}
+- MA20: ${data['MA20'].iloc[-1]:.2f}
+- RSI: {rsi.iloc[-1]:.2f}
+
+## 📋 最近5日数据
+{data.tail().to_string()}
+
+数据来源: Yahoo Finance API
+更新时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+"""
+
+ return result
+
+ def _try_get_old_cache(self, symbol: str, start_date: str, end_date: str) -> Optional[str]:
+ """尝试获取过期的缓存数据作为备用"""
+ try:
+ # 查找任何相关的缓存，不考虑TTL
+ for metadata_file in self.cache.metadata_dir.glob(f"*_meta.json"):
+ try:
+ import json
+ with open(metadata_file, 'r', encoding='utf-8') as f:
+ metadata = json.load(f)
- # Format and return
- formatted_data = self._format_stock_data(data, symbol)
- print(f"✅ Successfully fetched and cached data for {symbol}")
- return formatted_data
- else:
- print(f"⚠️ No data returned from Yahoo Finance for {symbol}")
-
- except Exception as e:
- print(f"❌ Yahoo Finance error for {symbol}: {e}")
-
- # Fallback: Try FINNHUB (if API key available)
- try:
- finnhub_data = self._fetch_from_finnhub(symbol, start_date, end_date)
- if finnhub_data:
- # Cache the string data
- cache_key = self.cache.save_stock_data(
- symbol, finnhub_data, start_date, end_date, "optimized_finnhub"
- )
- print(f"✅ Successfully fetched data from FINNHUB for {symbol}")
- return finnhub_data
-
- except Exception as e:
- print(f"❌ FINNHUB error for {symbol}: {e}")
-
- # If all fails, return error message
- error_msg = f"❌ Failed to fetch data for {symbol} from {start_date} to {end_date}"
- print(error_msg)
- return error_msg
-
+ if (metadata.get('symbol') == symbol and
+ metadata.get('data_type') == 'stock_data' and
+ metadata.get('market_type') == 'us'):
+
+ cache_key = metadata_file.stem.replace('_meta', '')
+ cached_data = self.cache.load_stock_data(cache_key)
+ if cached_data:
+ return cached_data + "\n\n⚠️ 注意: 使用的是过期缓存数据"
+ except Exception:
+ continue
+ except Exception:
+ pass
+
+ return None
+
+ def _get_data_from_finnhub(self, symbol: str, start_date: str, end_date: str) -> str:
+ """从FINNHUB API获取股票数据"""
+ try:
+ import finnhub
+ import os
+ from datetime import datetime, timedelta
+
+ # 获取API密钥
+ api_key = os.getenv('FINNHUB_API_KEY')
+ if not api_key:
+ return None
+
+ client = finnhub.Client(api_key=api_key)
+
+ # 获取实时报价
+ quote = client.quote(symbol.upper())
+ if not quote or 'c' not in quote:
+ return None
+
+ # 获取公司信息
+ profile = client.company_profile2(symbol=symbol.upper())
+ company_name = profile.get('name', symbol.upper()) if profile else symbol.upper()
+
+ # 格式化数据
+ current_price = quote.get('c', 0)
+ change = quote.get('d', 0)
+ change_percent = quote.get('dp', 0)
+
+ formatted_data = f"""# {symbol.upper()} 美股数据分析
+
+## 📊 实时行情
+- 股票名称: {company_name}
+- 当前价格: ${current_price:.2f}
+- 涨跌额: ${change:+.2f}
+- 涨跌幅: {change_percent:+.2f}%
+- 开盘价: ${quote.get('o', 0):.2f}
+- 最高价: ${quote.get('h', 0):.2f}
+- 最低价: ${quote.get('l', 0):.2f}
+- 前收盘: ${quote.get('pc', 0):.2f}
+- 更新时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+
+## 📈 数据概览
+- 数据期间: {start_date} 至 {end_date}
+- 数据来源: FINNHUB API (实时数据)
+- 当前价位相对位置: {((current_price - quote.get('l', current_price)) / max(quote.get('h', current_price) - quote.get('l', current_price), 0.01) * 100):.1f}%
+- 日内振幅: {((quote.get('h', 0) - quote.get('l', 0)) / max(quote.get('pc', 1), 0.01) * 100):.2f}%
+
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+"""
+
+ return formatted_data
+
except Exception as e:
- error_msg = f"❌ Unexpected error fetching data for {symbol}: {e}"
- print(error_msg)
- return error_msg
-
- def _fetch_from_yfinance(self, symbol: str, start_date: str, end_date: str) -> Optional[pd.DataFrame]:
- """Fetch data from Yahoo Finance"""
- try:
- ticker = yf.Ticker(symbol)
- data = ticker.history(start=start_date, end=end_date)
-
- if data.empty:
- print(f"⚠️ No data available for {symbol} in the specified date range")
- return None
-
- # Reset index to make Date a column
- data = data.reset_index()
-
- # Ensure we have the required columns
- required_columns = ['Date', 'Open', 'High', 'Low', 'Close', 'Volume']
- missing_columns = [col for col in required_columns if col not in data.columns]
-
- if missing_columns:
- print(f"⚠️ Missing columns for {symbol}: {missing_columns}")
- return None
-
- return data[required_columns]
-
- except Exception as e:
- print(f"❌ Yahoo Finance fetch error for {symbol}: {e}")
+ print(f"❌ FINNHUB数据获取失败: {e}")
return None
-
- def _fetch_from_finnhub(self, symbol: str, start_date: str, end_date: str) -> Optional[str]:
- """Fetch data from FINNHUB API"""
- try:
- # Check if FINNHUB API key is available
- finnhub_api_key = os.getenv('FINNHUB_API_KEY')
- if not finnhub_api_key:
- print("⚠️ FINNHUB API key not found, skipping FINNHUB data fetch")
- return None
-
- import finnhub
-
- # Initialize FINNHUB client
- finnhub_client = finnhub.Client(api_key=finnhub_api_key)
-
- # Convert dates to timestamps
- start_timestamp = int(datetime.strptime(start_date, '%Y-%m-%d').timestamp())
- end_timestamp = int(datetime.strptime(end_date, '%Y-%m-%d').timestamp())
-
- # Fetch candle data
- candle_data = finnhub_client.stock_candles(symbol, 'D', start_timestamp, end_timestamp)
-
- if candle_data['s'] != 'ok':
- print(f"⚠️ FINNHUB returned status: {candle_data['s']} for {symbol}")
- return None
-
- # Format data
- formatted_data = self._format_finnhub_data(candle_data, symbol)
- return formatted_data
-
- except ImportError:
- print("⚠️ finnhub-python package not installed, skipping FINNHUB data fetch")
- return None
- except Exception as e:
- print(f"❌ FINNHUB fetch error for {symbol}: {e}")
- return None
-
- def _format_stock_data(self, data: pd.DataFrame, symbol: str) -> str:
- """Format DataFrame stock data into string"""
- try:
- # Ensure Date column is properly formatted
- if 'Date' in data.columns:
- data['Date'] = pd.to_datetime(data['Date']).dt.strftime('%Y-%m-%d')
-
- # Round numerical columns to 2 decimal places
- numeric_columns = ['Open', 'High', 'Low', 'Close']
- for col in numeric_columns:
- if col in data.columns:
- data[col] = data[col].round(2)
-
- # Format volume as integer
- if 'Volume' in data.columns:
- data['Volume'] = data['Volume'].astype(int)
-
- # Create formatted string
- formatted_lines = [f"Stock Data for {symbol}:"]
- formatted_lines.append("Date,Open,High,Low,Close,Volume")
-
- for _, row in data.iterrows():
- line = f"{row['Date']},{row['Open']},{row['High']},{row['Low']},{row['Close']},{row['Volume']}"
- formatted_lines.append(line)
-
- # Add summary statistics
- if len(data) > 0:
- formatted_lines.append(f"\nSummary for {symbol}:")
- formatted_lines.append(f"Period: {data['Date'].iloc[0]} to {data['Date'].iloc[-1]}")
- formatted_lines.append(f"Total trading days: {len(data)}")
- formatted_lines.append(f"Average volume: {data['Volume'].mean():,.0f}")
- formatted_lines.append(f"Price range: ${data['Low'].min():.2f} - ${data['High'].max():.2f}")
-
- # Calculate basic statistics
- start_price = data['Open'].iloc[0]
- end_price = data['Close'].iloc[-1]
- price_change = end_price - start_price
- price_change_pct = (price_change / start_price) * 100
-
- formatted_lines.append(f"Period return: {price_change_pct:+.2f}% (${price_change:+.2f})")
-
- return "\n".join(formatted_lines)
-
- except Exception as e:
- print(f"❌ Error formatting stock data for {symbol}: {e}")
- return f"Error formatting data for {symbol}: {str(e)}"
-
- def _format_finnhub_data(self, candle_data: Dict, symbol: str) -> str:
- """Format FINNHUB candle data into string"""
- try:
- # Extract data arrays
- timestamps = candle_data['t']
- opens = candle_data['o']
- highs = candle_data['h']
- lows = candle_data['l']
- closes = candle_data['c']
- volumes = candle_data['v']
-
- # Create formatted string
- formatted_lines = [f"Stock Data for {symbol} (FINNHUB):"]
- formatted_lines.append("Date,Open,High,Low,Close,Volume")
-
- for i in range(len(timestamps)):
- date = datetime.fromtimestamp(timestamps[i]).strftime('%Y-%m-%d')
- line = f"{date},{opens[i]:.2f},{highs[i]:.2f},{lows[i]:.2f},{closes[i]:.2f},{int(volumes[i])}"
- formatted_lines.append(line)
-
- # Add summary
- if len(timestamps) > 0:
- start_date = datetime.fromtimestamp(timestamps[0]).strftime('%Y-%m-%d')
- end_date = datetime.fromtimestamp(timestamps[-1]).strftime('%Y-%m-%d')
-
- formatted_lines.append(f"\nSummary for {symbol}:")
- formatted_lines.append(f"Period: {start_date} to {end_date}")
- formatted_lines.append(f"Total trading days: {len(timestamps)}")
- formatted_lines.append(f"Average volume: {sum(volumes)/len(volumes):,.0f}")
- formatted_lines.append(f"Price range: ${min(lows):.2f} - ${max(highs):.2f}")
-
- # Calculate return
- price_change = closes[-1] - opens[0]
- price_change_pct = (price_change / opens[0]) * 100
- formatted_lines.append(f"Period return: {price_change_pct:+.2f}% (${price_change:+.2f})")
-
- return "\n".join(formatted_lines)
-
- except Exception as e:
- print(f"❌ Error formatting FINNHUB data for {symbol}: {e}")
- return f"Error formatting FINNHUB data for {symbol}: {str(e)}"
-
- def get_stock_with_indicators(self, symbol: str, start_date: str, end_date: str,
- indicators: list = None) -> str:
- """
- Get stock data with technical indicators
-
- Args:
- symbol: Stock symbol
- start_date: Start date (YYYY-MM-DD)
- end_date: End date (YYYY-MM-DD)
- indicators: List of indicators to calculate ['sma_20', 'rsi', 'macd']
-
- Returns:
- Formatted stock data with indicators
- """
- try:
- # Get basic stock data
- basic_data = self.get_stock_data(symbol, start_date, end_date)
-
- if basic_data.startswith("❌"):
- return basic_data
-
- # If no indicators requested, return basic data
- if not indicators:
- return basic_data
-
- # Fetch DataFrame for indicator calculation
- data_df = self._fetch_from_yfinance(symbol, start_date, end_date)
- if data_df is None or data_df.empty:
- return basic_data
-
- # Calculate indicators
- indicator_data = self._calculate_indicators(data_df, indicators)
-
- # Combine basic data with indicators
- combined_data = basic_data + "\n\nTechnical Indicators:\n" + indicator_data
-
- return combined_data
-
- except Exception as e:
- error_msg = f"❌ Error getting stock data with indicators for {symbol}: {e}"
- print(error_msg)
- return error_msg
-
- def _calculate_indicators(self, data: pd.DataFrame, indicators: list) -> str:
- """Calculate technical indicators"""
- try:
- indicator_lines = []
-
- for indicator in indicators:
- if indicator == 'sma_20':
- data['SMA_20'] = data['Close'].rolling(window=20).mean()
- latest_sma = data['SMA_20'].iloc[-1]
- indicator_lines.append(f"SMA(20): ${latest_sma:.2f}")
-
- elif indicator == 'rsi':
- # Simple RSI calculation
- delta = data['Close'].diff()
- gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
- loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
- rs = gain / loss
- rsi = 100 - (100 / (1 + rs))
- latest_rsi = rsi.iloc[-1]
- indicator_lines.append(f"RSI(14): {latest_rsi:.2f}")
-
- elif indicator == 'macd':
- # Simple MACD calculation
- ema_12 = data['Close'].ewm(span=12).mean()
- ema_26 = data['Close'].ewm(span=26).mean()
- macd_line = ema_12 - ema_26
- signal_line = macd_line.ewm(span=9).mean()
- latest_macd = macd_line.iloc[-1]
- latest_signal = signal_line.iloc[-1]
- indicator_lines.append(f"MACD: {latest_macd:.4f}, Signal: {latest_signal:.4f}")
-
- return "\n".join(indicator_lines)
-
- except Exception as e:
- print(f"❌ Error calculating indicators: {e}")
- return f"Error calculating indicators: {str(e)}"
-
-
-# Global provider instance
-_global_provider = None
+
+ def _generate_fallback_data(self, symbol: str, start_date: str, end_date: str, error_msg: str) -> str:
+ """生成备用数据"""
+ return f"""# {symbol} 美股数据获取失败
+
+## ❌ 错误信息
+{error_msg}
+
+## 📊 模拟数据（仅供演示）
+- 股票代码: {symbol}
+- 数据期间: {start_date} 至 {end_date}
+- 最新价格: ${random.uniform(100, 300):.2f}
+- 模拟涨跌: {random.uniform(-5, 5):+.2f}%
+
+## ⚠️ 重要提示
+由于API限制或网络问题，无法获取实时数据。
+建议稍后重试或检查网络连接。
+
+生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+"""
+
+
+# 全局实例
+_us_data_provider = None
def get_optimized_us_data_provider() -> OptimizedUSDataProvider:
+ """获取全局美股数据提供器实例"""
+ global _us_data_provider
+ if _us_data_provider is None:
+ _us_data_provider = OptimizedUSDataProvider()
+ return _us_data_provider
+
+
+def get_us_stock_data_cached(symbol: str, start_date: str, end_date: str,
+ force_refresh: bool = False) -> str:
"""
- Get global optimized US data provider instance
+ 获取美股数据的便捷函数
+
+ Args:
+ symbol: 股票代码
+ start_date: 开始日期 (YYYY-MM-DD)
+ end_date: 结束日期 (YYYY-MM-DD)
+ force_refresh: 是否强制刷新缓存
Returns:
- OptimizedUSDataProvider instance
+ 格式化的股票数据字符串
"""
- global _global_provider
- if _global_provider is None:
- _global_provider = OptimizedUSDataProvider()
- return _global_provider
-
-
-# Convenience functions
-def get_optimized_stock_data(symbol: str, start_date: str, end_date: str,
- force_refresh: bool = False) -> str:
- """Get optimized stock data (convenience function)"""
provider = get_optimized_us_data_provider()
return provider.get_stock_data(symbol, start_date, end_date, force_refresh)
-
-
-def get_stock_with_indicators(symbol: str, start_date: str, end_date: str,
- indicators: list = None) -> str:
- """Get stock data with technical indicators (convenience function)"""
- provider = get_optimized_us_data_provider()
- return provider.get_stock_with_indicators(symbol, start_date, end_date, indicators)
-
-
-if __name__ == "__main__":
- # Test the optimized data provider
- print("🧪 Testing Optimized US Data Provider...")
-
- # Initialize provider
- provider = OptimizedUSDataProvider()
-
- # Test data fetch
- data = provider.get_stock_data("AAPL", "2024-01-01", "2024-01-31")
- print("Sample data:")
- print(data[:500] + "..." if len(data) > 500 else data)
-
- # Test with indicators
- data_with_indicators = provider.get_stock_with_indicators(
- "AAPL", "2024-01-01", "2024-01-31",
- indicators=['sma_20', 'rsi', 'macd']
- )
- print("\nData with indicators:")
- print(data_with_indicators[-500:] if len(data_with_indicators) > 500 else data_with_indicators)
-
- print("✅ Optimized data provider test completed!")
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