Merge pull request #55 from aguzererler/fix/refactor-alpha-vantage-indicator-4324081028548110342
🧹 Refactor long `get_indicator` function to improve maintainability
This commit is contained in:
ahmet guzererler
GitHub
commit
ae83ce74fa
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@ -1,5 +1,167 @@
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from datetime import datetime
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from typing import Dict, Any
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from dateutil.relativedelta import relativedelta
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from .alpha_vantage_common import _make_api_request
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SUPPORTED_INDICATORS = {
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"close_50_sma": ("50 SMA", "close"),
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"close_200_sma": ("200 SMA", "close"),
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"close_10_ema": ("10 EMA", "close"),
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"macd": ("MACD", "close"),
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"macds": ("MACD Signal", "close"),
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"macdh": ("MACD Histogram", "close"),
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"rsi": ("RSI", "close"),
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"boll": ("Bollinger Middle", "close"),
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"boll_ub": ("Bollinger Upper Band", "close"),
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"boll_lb": ("Bollinger Lower Band", "close"),
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"atr": ("ATR", None),
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"vwma": ("VWMA", "close")
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}
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INDICATOR_DESCRIPTIONS = {
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"close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.",
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"close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.",
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"close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.",
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"macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.",
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"macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.",
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"macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.",
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"rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.",
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"boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.",
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"boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.",
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"boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.",
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"atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.",
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"vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
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}
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COL_NAME_MAP = {
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"macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist",
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"boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band",
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"rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA",
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"close_50_sma": "SMA", "close_200_sma": "SMA"
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}
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def _parse_indicator_data(
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data: str,
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indicator: str,
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before: datetime,
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curr_date_dt: datetime
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) -> tuple[str, list[tuple[datetime, str]]]:
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"""Helper function to parse the CSV string from Alpha Vantage."""
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lines = data.strip().split('\n')
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if len(lines) < 2:
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return f"Error: No data returned for {indicator}", []
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# Parse header and data
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header = [col.strip() for col in lines[0].split(',')]
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try:
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date_col_idx = header.index('time')
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except ValueError:
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return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}", []
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target_col_name = COL_NAME_MAP.get(indicator)
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if not target_col_name:
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# Default to the second column if no specific mapping exists
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value_col_idx = 1
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else:
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try:
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value_col_idx = header.index(target_col_name)
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except ValueError:
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return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}", []
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result_data = []
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for line in lines[1:]:
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if not line.strip():
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continue
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values = line.split(',')
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if len(values) > value_col_idx:
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try:
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date_str = values[date_col_idx].strip()
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# Parse the date
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date_dt = datetime.strptime(date_str, "%Y-%m-%d")
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# Check if date is in our range
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if before <= date_dt <= curr_date_dt:
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value = values[value_col_idx].strip()
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result_data.append((date_dt, value))
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except (ValueError, IndexError):
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continue
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return "", result_data
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def _fetch_indicator_data(
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symbol: str,
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indicator: str,
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interval: str,
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time_period: int,
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series_type: str
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) -> str:
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"""Helper function to fetch indicator data from Alpha Vantage."""
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if indicator == "close_50_sma":
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return _make_api_request("SMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "50",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "close_200_sma":
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return _make_api_request("SMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "200",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "close_10_ema":
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return _make_api_request("EMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "10",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator in ("macd", "macds", "macdh"):
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return _make_api_request("MACD", {
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"symbol": symbol,
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"interval": interval,
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "rsi":
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return _make_api_request("RSI", {
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"symbol": symbol,
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"interval": interval,
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"time_period": str(time_period),
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator in ["boll", "boll_ub", "boll_lb"]:
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return _make_api_request("BBANDS", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "20",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "atr":
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return _make_api_request("ATR", {
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"symbol": symbol,
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"interval": interval,
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"time_period": str(time_period),
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"datatype": "csv"
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})
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elif indicator == "vwma":
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# Alpha Vantage doesn't have direct VWMA, so we'll return an informative message
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# In a real implementation, this would need to be calculated from OHLCV data
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return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{INDICATOR_DESCRIPTIONS.get('vwma', 'No description available.')}"
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else:
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return f"Error: Indicator {indicator} not implemented yet."
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def get_indicator(
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symbol: str,
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indicator: str,
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@ -24,199 +186,41 @@ def get_indicator(
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Returns:
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String containing indicator values and description
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"""
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from datetime import datetime
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from dateutil.relativedelta import relativedelta
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supported_indicators = {
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"close_50_sma": ("50 SMA", "close"),
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"close_200_sma": ("200 SMA", "close"),
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"close_10_ema": ("10 EMA", "close"),
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"macd": ("MACD", "close"),
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"macds": ("MACD Signal", "close"),
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"macdh": ("MACD Histogram", "close"),
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"rsi": ("RSI", "close"),
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"boll": ("Bollinger Middle", "close"),
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"boll_ub": ("Bollinger Upper Band", "close"),
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"boll_lb": ("Bollinger Lower Band", "close"),
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"atr": ("ATR", None),
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"vwma": ("VWMA", "close")
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}
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indicator_descriptions = {
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"close_50_sma": "50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.",
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"close_200_sma": "200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.",
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"close_10_ema": "10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.",
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"macd": "MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.",
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"macds": "MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.",
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"macdh": "MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.",
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"rsi": "RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.",
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"boll": "Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.",
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"boll_ub": "Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.",
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"boll_lb": "Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.",
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"atr": "ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.",
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"vwma": "VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses."
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}
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if indicator not in supported_indicators:
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if indicator not in SUPPORTED_INDICATORS:
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raise ValueError(
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f"Indicator {indicator} is not supported. Please choose from: {list(supported_indicators.keys())}"
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f"Indicator {indicator} is not supported. Please choose from: {list(SUPPORTED_INDICATORS.keys())}"
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)
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curr_date_dt = datetime.strptime(curr_date, "%Y-%m-%d")
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before = curr_date_dt - relativedelta(days=look_back_days)
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# Get the full data for the period instead of making individual calls
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_, required_series_type = supported_indicators[indicator]
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# Use the provided series_type or fall back to the required one
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_, required_series_type = SUPPORTED_INDICATORS[indicator]
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if required_series_type:
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series_type = required_series_type
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try:
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# Get indicator data for the period
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if indicator == "close_50_sma":
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data = _make_api_request("SMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "50",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "close_200_sma":
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data = _make_api_request("SMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "200",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "close_10_ema":
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data = _make_api_request("EMA", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "10",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "macd":
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data = _make_api_request("MACD", {
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"symbol": symbol,
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"interval": interval,
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "macds":
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data = _make_api_request("MACD", {
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"symbol": symbol,
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"interval": interval,
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "macdh":
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data = _make_api_request("MACD", {
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"symbol": symbol,
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"interval": interval,
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "rsi":
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data = _make_api_request("RSI", {
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"symbol": symbol,
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"interval": interval,
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"time_period": str(time_period),
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator in ["boll", "boll_ub", "boll_lb"]:
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data = _make_api_request("BBANDS", {
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"symbol": symbol,
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"interval": interval,
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"time_period": "20",
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"series_type": series_type,
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"datatype": "csv"
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})
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elif indicator == "atr":
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data = _make_api_request("ATR", {
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"symbol": symbol,
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"interval": interval,
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"time_period": str(time_period),
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"datatype": "csv"
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})
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elif indicator == "vwma":
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# Alpha Vantage doesn't have direct VWMA, so we'll return an informative message
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# In a real implementation, this would need to be calculated from OHLCV data
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return f"## VWMA (Volume Weighted Moving Average) for {symbol}:\n\nVWMA calculation requires OHLCV data and is not directly available from Alpha Vantage API.\nThis indicator would need to be calculated from the raw stock data using volume-weighted price averaging.\n\n{indicator_descriptions.get('vwma', 'No description available.')}"
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else:
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return f"Error: Indicator {indicator} not implemented yet."
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data = _fetch_indicator_data(symbol, indicator, interval, time_period, series_type)
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if data.startswith("Error:") or data.startswith("## VWMA"):
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return data
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# Parse CSV data and extract values for the date range
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lines = data.strip().split('\n')
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if len(lines) < 2:
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return f"Error: No data returned for {indicator}"
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err_msg, result_data = _parse_indicator_data(data, indicator, before, curr_date_dt)
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if err_msg:
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return err_msg
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# Parse header and data
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header = [col.strip() for col in lines[0].split(',')]
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try:
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date_col_idx = header.index('time')
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except ValueError:
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return f"Error: 'time' column not found in data for {indicator}. Available columns: {header}"
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# Map internal indicator names to expected CSV column names from Alpha Vantage
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col_name_map = {
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"macd": "MACD", "macds": "MACD_Signal", "macdh": "MACD_Hist",
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"boll": "Real Middle Band", "boll_ub": "Real Upper Band", "boll_lb": "Real Lower Band",
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"rsi": "RSI", "atr": "ATR", "close_10_ema": "EMA",
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"close_50_sma": "SMA", "close_200_sma": "SMA"
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}
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target_col_name = col_name_map.get(indicator)
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if not target_col_name:
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# Default to the second column if no specific mapping exists
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value_col_idx = 1
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else:
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try:
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value_col_idx = header.index(target_col_name)
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except ValueError:
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return f"Error: Column '{target_col_name}' not found for indicator '{indicator}'. Available columns: {header}"
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result_data = []
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for line in lines[1:]:
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if not line.strip():
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continue
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values = line.split(',')
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if len(values) > value_col_idx:
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try:
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date_str = values[date_col_idx].strip()
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# Parse the date
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date_dt = datetime.strptime(date_str, "%Y-%m-%d")
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# Check if date is in our range
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if before <= date_dt <= curr_date_dt:
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value = values[value_col_idx].strip()
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result_data.append((date_dt, value))
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except (ValueError, IndexError):
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continue
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# Sort by date and format output
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result_data.sort(key=lambda x: x[0])
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ind_string = ""
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for date_dt, value in result_data:
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ind_string += f"{date_dt.strftime('%Y-%m-%d')}: {value}\n"
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if not ind_string:
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ind_string = "\n".join([f"{date_dt.strftime('%Y-%m-%d')}: {value}" for date_dt, value in result_data])
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if ind_string:
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ind_string += "\n"
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else:
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ind_string = "No data available for the specified date range.\n"
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result_str = (
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return (
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f"## {indicator.upper()} values from {before.strftime('%Y-%m-%d')} to {curr_date}:\n\n"
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+ ind_string
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+ "\n\n"
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+ indicator_descriptions.get(indicator, "No description available.")
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f"{ind_string}\n\n"
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f"{INDICATOR_DESCRIPTIONS.get(indicator, 'No description available.')}"
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)
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return result_str
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except Exception as e:
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print(f"Error getting Alpha Vantage indicator data for {indicator}: {e}")
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return f"Error retrieving {indicator} data: {str(e)}"
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