Refactor `get_indicator` function in `alpha_vantage_indicator.py`
This change refactors the large monolithic `get_indicator` function by extracting large mapping dictionaries out of the function scope as constants and separating API fetching logic and CSV parsing logic into private helper functions. This drastically improves the maintainability and readability of `alpha_vantage_indicator.py` without introducing any behavioral changes. Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
This commit is contained in:
google-labs-jules[bot]
parent
5799bb3f00
commit
0a53060ce9
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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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