support get price data from alpha vantage
This commit is contained in:
auwc
parent
a438acdbbd
commit
86b357d77a
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@ -123,6 +123,9 @@ You will need the OpenAI API for all the agents.
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```bash
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export OPENAI_API_KEY=$YOUR_OPENAI_API_KEY
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```
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**Optional** Stock Price Data
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Since YFinance may not work, you can add alpha vantage api key in default_config.py.
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### CLI Usage
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@ -14,6 +14,8 @@ def create_market_analyst(llm, toolkit):
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tools = [
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toolkit.get_YFin_data_online,
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toolkit.get_stockstats_indicators_report_online,
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toolkit.get_alpha_vantage_data,
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toolkit.get_alpha_vantage_summary_signals,
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]
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else:
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tools = [
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@ -161,6 +161,45 @@ class Toolkit:
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return result_data
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@staticmethod
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@tool
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def get_alpha_vantage_data(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"],
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) -> str:
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"""
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Retrieve the stock price data for a given ticker symbol from Alpha Vantage.
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Args:
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symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
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start_date (str): Start date in yyyy-mm-dd format
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end_date (str): End date in yyyy-mm-dd format
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Returns:
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str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
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"""
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result_data = interface.get_alpha_vantage_data(symbol, start_date, end_date)
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return result_data
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@staticmethod
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@tool
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def get_alpha_vantage_summary_signals(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"]
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) -> str:
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"""
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Calculate technical indicators and signals based on stock price data from Alpha Vantage.
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Args:
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symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
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start_date (str): Start date in yyyy-mm-dd format
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end_date (str): End date in yyyy-mm-dd format
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Returns:
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str: Formatted technical indicators summary and signals.
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"""
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result_data = interface.get_alpha_vantage_summary_signals(symbol, start_date, end_date)
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return result_data
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@staticmethod
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@tool
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def get_stockstats_indicators_report(
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@ -805,3 +805,328 @@ def get_fundamentals_openai(ticker, curr_date):
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)
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return response.output[1].content[0].text
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def get_alpha_vantage_data(symbol, start_date, end_date):
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config = get_config()
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api_key = config["alpha_vantage_api_key"]
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url = "https://www.alphavantage.co/query"
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params = {
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'function': 'TIME_SERIES_DAILY',
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'symbol': symbol,
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'outputsize': 'full',
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'apikey': api_key
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}
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response = requests.get(url, params=params)
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data = response.json()
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print("API 响应:", list(data.keys())) # 调试信息
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if 'Time Series (Daily)' in data:
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df = pd.DataFrame(data['Time Series (Daily)']).T
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df.index = pd.to_datetime(df.index)
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df = df.sort_index()
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# 重命名列并转换数据类型
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df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
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for col in df.columns:
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df[col] = pd.to_numeric(df[col])
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# 过滤日期范围
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if start_date:
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df = df[df.index >= start_date]
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if end_date:
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df = df[df.index <= end_date]
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# # Add header information
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# header = f"# Stock data for {symbol.upper()} from {start_date} to {end_date}\n"
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# header += f"# Total records: {len(data)}\n"
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# header += f"# Data retrieved on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
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return df
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else:
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print("错误或限制:", data)
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return None
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def add_technical_indicators(df):
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"""
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为股票数据添加技术指标
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参数:
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df: DataFrame with columns ['Open', 'High', 'Low', 'Close', 'Volume']
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返回:
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DataFrame with additional technical indicator columns
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"""
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# 创建副本以避免修改原数据
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data = df.copy()
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# 确保数据按日期排序
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data = data.sort_index()
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# =============================================================================
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# Moving Averages 移动平均线
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# =============================================================================
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# 50日简单移动平均线
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data['close_50_sma'] = data['Close'].rolling(window=50).mean()
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# 200日简单移动平均线
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data['close_200_sma'] = data['Close'].rolling(window=200).mean()
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# 10日指数移动平均线
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data['close_10_ema'] = data['Close'].ewm(span=10).mean()
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# =============================================================================
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# MACD 指标
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# =============================================================================
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# 计算12日和26日EMA
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ema_12 = data['Close'].ewm(span=12).mean()
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ema_26 = data['Close'].ewm(span=26).mean()
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# MACD线 = 12EMA - 26EMA
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data['macd'] = ema_12 - ema_26
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# MACD信号线 = MACD的9日EMA
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data['macds'] = data['macd'].ewm(span=9).mean()
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# MACD柱状图 = MACD - 信号线
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data['macdh'] = data['macd'] - data['macds']
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# =============================================================================
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# RSI 相对强弱指数
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# =============================================================================
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def calculate_rsi(prices, period=14):
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"""计算RSI"""
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delta = prices.diff()
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gain = delta.where(delta > 0, 0)
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loss = -delta.where(delta < 0, 0)
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avg_gain = gain.rolling(window=period).mean()
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avg_loss = loss.rolling(window=period).mean()
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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data['rsi'] = calculate_rsi(data['Close'])
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# =============================================================================
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# Bollinger Bands 布林带
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# =============================================================================
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# 布林带中线（20日SMA）
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data['boll'] = data['Close'].rolling(window=20).mean()
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# 计算20日标准差
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std_20 = data['Close'].rolling(window=20).std()
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# 布林带上轨 = 中线 + 2倍标准差
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data['boll_ub'] = data['boll'] + (2 * std_20)
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# 布林带下轨 = 中线 - 2倍标准差
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data['boll_lb'] = data['boll'] - (2 * std_20)
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# =============================================================================
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# ATR 平均真实波幅
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# =============================================================================
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def calculate_atr(high, low, close, period=14):
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"""计算ATR"""
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# 真实波幅的三种计算方式
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tr1 = high - low
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tr2 = abs(high - close.shift(1))
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tr3 = abs(low - close.shift(1))
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# 取最大值作为真实波幅
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true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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# ATR = 真实波幅的移动平均
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atr = true_range.rolling(window=period).mean()
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return atr
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data['atr'] = calculate_atr(data['High'], data['Low'], data['Close'])
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# =============================================================================
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# VWMA 成交量加权移动平均
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# =============================================================================
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def calculate_vwma(close, volume, period=20):
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"""计算成交量加权移动平均"""
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volume_price = close * volume
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vwma = volume_price.rolling(window=period).sum() / volume.rolling(window=period).sum()
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return vwma
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data['vwma'] = calculate_vwma(data['Close'], data['Volume'])
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# =============================================================================
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# 额外计算一些有用的信号
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# =============================================================================
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# 金叉死叉信号 (50 SMA vs 200 SMA)
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data['golden_cross'] = (data['close_50_sma'] > data['close_200_sma']) & \
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(data['close_50_sma'].shift(1) <= data['close_200_sma'].shift(1))
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data['death_cross'] = (data['close_50_sma'] < data['close_200_sma']) & \
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(data['close_50_sma'].shift(1) >= data['close_200_sma'].shift(1))
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# MACD信号
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data['macd_bullish'] = (data['macd'] > data['macds']) & \
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(data['macd'].shift(1) <= data['macds'].shift(1))
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data['macd_bearish'] = (data['macd'] < data['macds']) & \
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(data['macd'].shift(1) >= data['macds'].shift(1))
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# RSI信号
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data['rsi_overbought'] = data['rsi'] > 70
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data['rsi_oversold'] = data['rsi'] < 30
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# 布林带信号
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data['price_above_upper_band'] = data['Close'] > data['boll_ub']
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data['price_below_lower_band'] = data['Close'] < data['boll_lb']
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# 显示统计信息
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total_indicators = 11 # 主要技术指标数量
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valid_rows = len(data.dropna())
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return data
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def display_indicator_summary(df):
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"""Generate technical indicator summary string"""
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latest = df.iloc[-1] # Latest data
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summary = f"\n📈 Latest Technical Indicator Summary ({latest.name.date()}):\n"
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summary += "=" * 50 + "\n"
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# Price information
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summary += f"Close Price: ${latest['Close']:.2f}\n"
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# Moving averages
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summary += f"\n📊 Moving Averages:\n"
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if not pd.isna(latest['close_10_ema']):
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summary += f" 10-day EMA: ${latest['close_10_ema']:.2f}\n"
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if not pd.isna(latest['close_50_sma']):
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summary += f" 50-day SMA: ${latest['close_50_sma']:.2f}\n"
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if not pd.isna(latest['close_200_sma']):
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summary += f" 200-day SMA: ${latest['close_200_sma']:.2f}\n"
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# Trend signals
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if not pd.isna(latest['close_50_sma']) and not pd.isna(latest['close_200_sma']):
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trend = "Uptrend 📈" if latest['close_50_sma'] > latest['close_200_sma'] else "Downtrend 📉"
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summary += f" Long-term Trend: {trend}\n"
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# MACD
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summary += f"\n📈 MACD:\n"
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if not pd.isna(latest['macd']):
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summary += f" MACD: {latest['macd']:.4f}\n"
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summary += f" Signal Line: {latest['macds']:.4f}\n"
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summary += f" Histogram: {latest['macdh']:.4f}\n"
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if latest['macd'] > latest['macds']:
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summary += " 📈 MACD Bullish Signal\n"
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else:
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summary += " 📉 MACD Bearish Signal\n"
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# RSI
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summary += f"\n⚡ RSI:\n"
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if not pd.isna(latest['rsi']):
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summary += f" RSI(14): {latest['rsi']:.2f}\n"
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if latest['rsi'] > 70:
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summary += " ⚠️ Overbought Zone\n"
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elif latest['rsi'] < 30:
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summary += " ⚠️ Oversold Zone\n"
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else:
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summary += " ✅ Normal Zone\n"
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# Bollinger Bands
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summary += f"\n📊 Bollinger Bands:\n"
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if not pd.isna(latest['boll']):
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summary += f" Upper Band: ${latest['boll_ub']:.2f}\n"
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summary += f" Middle Band: ${latest['boll']:.2f}\n"
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summary += f" Lower Band: ${latest['boll_lb']:.2f}\n"
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if latest['Close'] > latest['boll_ub']:
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summary += " 📈 Price Above Upper Band\n"
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elif latest['Close'] < latest['boll_lb']:
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summary += " 📉 Price Below Lower Band\n"
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else:
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summary += " ✅ Price Within Bands\n"
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# ATR and VWMA
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summary += f"\n📊 Other Indicators:\n"
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if not pd.isna(latest['atr']):
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summary += f" ATR(14): {latest['atr']:.2f} (Volatility)\n"
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if not pd.isna(latest['vwma']):
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summary += f" VWMA(20): ${latest['vwma']:.2f} (Volume-Weighted MA)\n"
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return summary
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def get_trading_signals(df):
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"""Get recent trading signals as string"""
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recent_data = df.tail(5) # Last 5 days
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signals = []
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for date, row in recent_data.iterrows():
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day_signals = []
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if row.get('golden_cross', False):
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day_signals.append("🟢 Golden Cross")
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if row.get('death_cross', False):
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day_signals.append("🔴 Death Cross")
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if row.get('macd_bullish', False):
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day_signals.append("📈 MACD Bullish")
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if row.get('macd_bearish', False):
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day_signals.append("📉 MACD Bearish")
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if row.get('rsi_overbought', False):
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day_signals.append("⚠️ RSI Overbought")
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if row.get('rsi_oversold', False):
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day_signals.append("⚠️ RSI Oversold")
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if row.get('price_above_upper_band', False):
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day_signals.append("📈 Above Upper Band")
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if row.get('price_below_lower_band', False):
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day_signals.append("📉 Below Lower Band")
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if day_signals:
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signals.append(f"{date.date()}: {', '.join(day_signals)}")
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if signals:
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result = "\n🚨 Recent Trading Signals:\n"
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result += "=" * 30 + "\n"
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for signal in signals:
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result += f" {signal}\n"
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return result
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else:
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return "\n✅ No significant trading signals in the last 5 days\n"
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def get_alpha_vantage_summary_signals(symbol, start_date, end_date):
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# 1. 获取股票数据 (使用前面的Alpha Vantage函数)
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data = get_alpha_vantage_data(symbol, start_date, end_date)
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# 2. 添加技术指标
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data_with_indicators = add_technical_indicators(data)
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# 3. 查看指标摘要
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summary = display_indicator_summary(data_with_indicators)
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# 4. 查看交易信号
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signals = get_trading_signals(data_with_indicators)
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# 5. 查看特定指标
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df_tail = data_with_indicators[['Close', 'rsi', 'macd', 'close_50_sma']].tail()
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tail_string = "\nRecent Prices\n" + df_tail.to_string()
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return summary + signals + tail_string
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if __name__ == "__main__":
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text = get_alpha_vantage_summary_signals('NVDA', '2024-01-01', '2024-12-31')
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print(text)
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@ -19,4 +19,5 @@ DEFAULT_CONFIG = {
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"max_recur_limit": 100,
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# Tool settings
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"online_tools": True,
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"alpha_vantage_api_key": "$YOUR_API_KEY"
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}
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@ -117,6 +117,8 @@ class TradingAgentsGraph:
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# online tools
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self.toolkit.get_YFin_data_online,
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self.toolkit.get_stockstats_indicators_report_online,
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self.toolkit.get_alpha_vantage_data,
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self.toolkit.get_alpha_vantage_summary_signals,
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# offline tools
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self.toolkit.get_YFin_data,
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self.toolkit.get_stockstats_indicators_report,
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@ -127,7 +129,7 @@ class TradingAgentsGraph:
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# online tools
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self.toolkit.get_stock_news_openai,
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# offline tools
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self.toolkit.get_reddit_stock_info,
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# self.toolkit.get_reddit_stock_info,
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]
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),
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"news": ToolNode(
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@ -136,8 +138,8 @@ class TradingAgentsGraph:
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self.toolkit.get_global_news_openai,
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self.toolkit.get_google_news,
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# offline tools
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self.toolkit.get_finnhub_news,
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self.toolkit.get_reddit_news,
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# self.toolkit.get_finnhub_news,
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# self.toolkit.get_reddit_news,
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]
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),
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"fundamentals": ToolNode(
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@ -145,11 +147,11 @@ class TradingAgentsGraph:
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# online tools
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self.toolkit.get_fundamentals_openai,
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# offline tools
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self.toolkit.get_finnhub_company_insider_sentiment,
|
||||
self.toolkit.get_finnhub_company_insider_transactions,
|
||||
self.toolkit.get_simfin_balance_sheet,
|
||||
self.toolkit.get_simfin_cashflow,
|
||||
self.toolkit.get_simfin_income_stmt,
|
||||
# self.toolkit.get_finnhub_company_insider_sentiment,
|
||||
# self.toolkit.get_finnhub_company_insider_transactions,
|
||||
# self.toolkit.get_simfin_balance_sheet,
|
||||
# self.toolkit.get_simfin_cashflow,
|
||||
# self.toolkit.get_simfin_income_stmt,
|
||||
]
|
||||
),
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue