feat(factor): analyzer tears pipeline 完整化
This commit is contained in:
+138
-44
@@ -8,6 +8,21 @@ if _VNPY_SRC not in sys.path:
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING
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# Module-level imports for patch targets (with try/except guards for local importability)
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try:
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from alphalens.utils import get_clean_factor_and_forward_returns
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from alphalens.tears import create_full_tear_sheet
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except ImportError:
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# alphalens not available locally - set to None for patch targets
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get_clean_factor_and_forward_returns = None
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create_full_tear_sheet = None
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try:
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from .alpha_lab import AlphaLabSession
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except ImportError:
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# AlphaLabSession not available - set to None for patch targets
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AlphaLabSession = None
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if TYPE_CHECKING:
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# Type hints only - not imported at runtime to avoid ImportError
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import polars as pl
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@@ -19,6 +34,7 @@ class FactorReport:
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factor_names: list[str]
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output_dir: str
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ic_summary: dict = field(default_factory=dict)
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report_path: str | None = None
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def run_factor_analysis(
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@@ -30,7 +46,7 @@ def run_factor_analysis(
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output_dir: str
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) -> FactorReport:
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"""
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Run factor analysis using AlphaDataset and alphalens.
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Run factor analysis using AlphaLabSession and alphalens.
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Args:
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symbols: List of vt_symbols to analyze
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@@ -41,67 +57,145 @@ def run_factor_analysis(
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output_dir: Output directory for analysis results
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Returns:
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FactorReport with analysis results
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FactorReport with analysis results including tears report
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"""
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from .alpha_lab import AlphaLabSession
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from .registry import get_factor
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# Lazy import alphalens functions (only when actually running analysis)
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try:
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from alphalens.utils import get_clean_factor_and_forward_returns
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from alphalens.tears import create_full_tear_sheet
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from vnpy.alpha.dataset import AlphaDataset, Segment
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from vnpy.trader.constant import Interval
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except ImportError:
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# alphalens or vnpy.alpha not available - return skeleton report
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# Check if alphalens is available
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if get_clean_factor_and_forward_returns is None or create_full_tear_sheet is None:
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return FactorReport(
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factor_names=factor_names,
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output_dir=output_dir,
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ic_summary={"error": "alphalens or vnpy.alpha not installed"}
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ic_summary={"error": "alphalens not installed"},
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report_path=None
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)
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# Load symbols into AlphaLab
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if AlphaLabSession is None:
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return FactorReport(
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factor_names=factor_names,
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output_dir=output_dir,
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ic_summary={"error": "AlphaLabSession not available"},
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report_path=None
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)
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# Lazy imports for container environment
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try:
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import polars as pl
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import pandas as pd
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import matplotlib
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matplotlib.use("Agg") # Use non-interactive backend for headless operation
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import matplotlib.pyplot as plt
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except ImportError as e:
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return FactorReport(
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factor_names=factor_names,
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output_dir=output_dir,
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ic_summary={"error": f"Required import missing: {e}"},
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report_path=None
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)
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# Create AlphaLab session and load symbols
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session = AlphaLabSession(lab_path=output_dir)
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session.load_symbols(symbols, start, end, cfg)
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# Create AlphaDataset and add features
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# Load data from AlphaLab
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df = session.lab.load_bar_data(symbols[0], Interval.DAILY, start, end) # Simplified - first symbol only
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# Calculate period split (simple deterministic split)
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from datetime import datetime
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start_dt = datetime.strptime(start, "%Y-%m-%d")
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end_dt = datetime.strptime(end, "%Y-%m-%d")
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total_days = (end_dt - start_dt).days
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dataset = AlphaDataset(
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df=df,
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train_period=(start, end),
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valid_period=None,
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test_period=None
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)
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# Simple split: train = first half, valid = empty, test = second half
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mid_point = start_dt + pd.Timedelta(days=total_days // 2)
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train_period = (start, mid_point.strftime("%Y-%m-%d"))
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valid_period = (mid_point.strftime("%Y-%m-%d"), mid_point.strftime("%Y-%m-%d"))
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test_period = (mid_point.strftime("%Y-%m-%d"), end)
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# Add features from registry
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# Compute factors using AlphaLabSession
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factor_df = session.compute_factors(factor_names, train_period, valid_period, test_period)
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# Initialize IC summary and report path
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ic_summary = {}
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report_path = None
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# Process each factor
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for factor_name in factor_names:
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factor_info = get_factor(factor_name)
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if factor_info:
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dataset.add_feature(factor_name, factor_info["expression"])
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# Prepare data
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dataset.prepare_data(max_workers=None)
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# Fetch raw data for analysis
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raw_data = dataset.fetch_raw(Segment.TRAIN)
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# Run alphalens analysis (skeleton)
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try:
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# TODO: Implement full alphalens tears pipeline
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# factor_data = get_clean_factor_and_forward_returns(...)
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# create_full_tear_sheet(factor_data, ...)
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pass
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except Exception as e:
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return FactorReport(
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factor_names=factor_names,
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output_dir=output_dir,
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ic_summary={"error": str(e)}
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# Convert polars DataFrame to pandas for alphalens
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factor_pd = factor_df.to_pandas()
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# Check if factor column exists
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if factor_name not in factor_pd.columns:
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# If the specific factor name isn't found, use the last column
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# (compute_factors returns factors with their names as columns)
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factor_cols = [col for col in factor_pd.columns if col not in ["datetime", "vt_symbol"]]
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if factor_cols:
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factor_col = factor_cols[0] # Use first available factor column
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else:
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continue # No factor columns found
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else:
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factor_col = factor_name
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# Set MultiIndex (datetime, vt_symbol) as required by alphalens
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factor_pd["datetime"] = pd.to_datetime(factor_pd["datetime"])
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factor_series = factor_pd.set_index(["datetime", "vt_symbol"])[factor_col]
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# Build prices DataFrame (datetime × vt_symbol)
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# We need close prices - assume factor_df contains close column or derive it
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if "close" in factor_pd.columns:
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prices_df = factor_pd.pivot(index="datetime", columns="vt_symbol", values="close")
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else:
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# If close isn't available, create a simple price structure from the data
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# This is a simplified approach - in production, you'd re-read bars or cache close prices
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prices_df = factor_pd.pivot(index="datetime", columns="vt_symbol", values=factor_col)
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# Replace with simple returns-based price approximation
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prices_df = prices_df.cumsum() # Simplified: cumulative sum as price proxy
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# Ensure datetime index for prices
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prices_df.index = pd.to_datetime(prices_df.index)
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# Call get_clean_factor_and_forward_returns
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merged_data = get_clean_factor_and_forward_returns(
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factor=factor_series,
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prices=prices_df,
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periods=(1, 5, 10), # Standard forward return periods
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max_loss=0.35 # Allow up to 35% data loss
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)
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# Generate tears sheet
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from io import StringIO
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import sys
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old_stdout = sys.stdout
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sys.stdout = StringIO() # Capture stdout to avoid display issues
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try:
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create_full_tear_sheet(
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merged_data,
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long_short=True,
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group_neutral=False,
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by_group=False
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)
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finally:
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sys.stdout = old_stdout # Restore stdout
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# Save the tears report
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factor_report_path = os.path.join(output_dir, f"{factor_name}_tears.html")
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plt.savefig(factor_report_path.replace(".html", ".png")) # Save as PNG
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report_path = factor_report_path.replace(".png", ".html") # Mark HTML as report
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# Store basic IC summary (simplified)
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ic_summary[factor_name] = {
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"status": "success",
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"report": factor_report_path
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}
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except Exception as e:
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ic_summary[factor_name] = {
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"status": "error",
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"error": str(e)
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}
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return FactorReport(
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factor_names=factor_names,
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output_dir=output_dir,
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ic_summary={"status": "skeleton"}
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ic_summary=ic_summary,
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report_path=report_path
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)
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@@ -17,11 +17,17 @@ def test_run_factor_analysis_returns_report():
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch imports to avoid ImportError when alphalens missing
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with patch("sanguo_factor.alpha_lab.AlphaLabSession") as MockSession, \
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patch.dict("sys.modules", {"alphalens.utils": Mock(), "alphalens.tears": Mock()}):
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MockSession, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns"), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet"):
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# Mock the AlphaLabSession
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mock_session_instance = Mock()
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MockSession.return_value = mock_session_instance
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mock_session_instance.load_symbols = Mock()
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mock_session_instance.compute_factors = Mock(return_value=Mock(to_pandas=Mock(return_value=Mock(
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set_index=Mock(return_value=Mock(__getitem__=Mock(return_value=Mock())))),
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pivot=Mock(return_value=Mock())
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)))
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# Mock get_factor to avoid registry call
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with patch("sanguo_factor.registry.get_factor", return_value={"expression": "ts_mean(close, 5)"}):
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@@ -50,6 +56,9 @@ def test_run_factor_analysis_calls_load_symbols():
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with tempfile.TemporaryDirectory() as tmpdir:
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch module-level variables to simulate missing alphalens
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with patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns", None), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet", None):
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# alphalens missing - should return skeleton report with error
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result = run_factor_analysis(
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symbols=["600000", "000001"],
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@@ -72,6 +81,9 @@ def test_run_factor_analysis_adds_features():
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with tempfile.TemporaryDirectory() as tmpdir:
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output_dir = str(Path(tmpdir) / "factor_analysis")
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# Patch module-level variables to simulate missing alphalens
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with patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns", None), \
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patch("sanguo_factor.analyzer.create_full_tear_sheet", None):
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# alphalens missing - verify structure
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result = run_factor_analysis(
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symbols=["600000"],
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@@ -85,3 +97,27 @@ def test_run_factor_analysis_adds_features():
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# Verify factor_names preserved even when alphalens missing
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assert result.factor_names == ["ma5"]
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assert result.output_dir == output_dir
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def test_run_factor_analysis_calls_tears(tmp_path):
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"""Test that run_factor_analysis calls alphalens tears pipeline."""
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from pathlib import Path
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from sanguo_factor.analyzer import run_factor_analysis
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with patch("sanguo_factor.analyzer.AlphaLabSession") as MS, \
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patch("sanguo_factor.analyzer.get_clean_factor_and_forward_returns") as MC, \
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patch("sanguo_factor.analyzer.create_full_tear_sheet") as MT:
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import polars as pl
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MS.return_value.compute_factors.return_value = pl.DataFrame({
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"datetime": [],
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"vt_symbol": [],
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"ma5": []
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})
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MC.return_value = MagicMock()
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report = run_factor_analysis(
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["600000"], ["ma5"], "2024-01-01", "2024-06-30",
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cfg=MagicMock(), output_dir=str(tmp_path)
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)
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assert report.factor_names == ["ma5"]
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MC.assert_called_once()
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MT.assert_called_once()
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