Strategy Development Manager

Purpose

SDM orchestrates the full lifecycle from academic paper or research report to validated factor or strategy. It ingests documents, extracts quantitative signals, implements and backtests them through the existing tool chain, evaluates results against statistical thresholds, and monitors long-term decay. SDM does not reinvent any step. It delegates to the tools already available (read_document, factor_analysis, backtest, alpha_bench, and the hypothesis/autopilot stack) and adds a thin coordination layer with persistent artifact tracking.

Use this skill whenever a user wants to go from "here is a paper" to "I have a working, monitored factor or strategy in the system."

When to Use

Decision tree for routing user requests:

• User provides a paper or report path → Phase 1: INGEST
• User says "extract factors from this paper" → Phase 2: EXTRACT
• User says "implement and backtest" or "run the backtest" → Phase 3: IMPLEMENT
• User says "evaluate results" or "check if it works" → Phase 4: EVALUATE
• User says "check decay" or "monitor factors" → Phase 5: MONITOR
• User says "disable factor" → sdm_status(action="disable", artifact_id=...)
• User says "enable factor" → sdm_status(action="enable", artifact_id=...)
• User says "list my factors" or "show status" → sdm_status(action="list")

When the user's intent spans multiple phases (for example "read this paper and build a factor"), run the phases sequentially from INGEST through EVALUATE.

Workflow

Phase 1: INGEST

Parse the source document and classify its content.

1. Call read_document(paper_path) to extract the full text from the PDF or report.
2. Classify the paper type:
   • factor-research: the paper proposes one or more cross-sectional factors with formulas (for example Jegadeesh and Titman 1993, Fama-French 1993)
   • strategy: the paper describes entry/exit rules, position sizing, and risk management (for example Avramov and Chordia 2006, turtle trading)
   • mixed: the paper contains both factor definitions and strategy rules
3. Extract key information from the parsed text:
   • Methodology description
   • Mathematical formulas (preserve LaTeX notation)
   • Variable definitions and data requirements
   • Universe and time period studied
   • Performance metrics reported in the paper

Phase 2: EXTRACT

Turn the parsed content into structured artifact definitions.

1. For factors, extract:

   • name: short identifier (for example "momentum_12_1")
   • formula_latex: the mathematical formula as written in the paper
   • variables: list of input variables and their meanings
   • columns_required: OHLCV columns or fundamental fields needed
   • universe: target market (for example "equity_us", "equity_cn")
   • decay_horizon: recommended holding period in trading days

2. For strategies, extract:

   • name: short identifier
   • entry_rules: conditions that trigger a long or short position
   • exit_rules: conditions that close a position
   • position_sizing: how to allocate capital across selected instruments
   • risk_management: stop-loss, max drawdown, exposure limits
   • universe: target market
   • columns_required: data fields needed

3. Deduplication check: call alpha_bench or check sdm_status(action="list") to see if a similar artifact already exists. If the Pearson IC between the new factor and an existing alpha exceeds 0.99, treat it as a duplicate and stop. IC between 0.90 and 0.99 may be a variant worth keeping with a note.

4. Register the artifact: call sdm_register(artifact_type, name, universe, ...) to persist the extracted definition with status "extracted".

OCR Quality Check

After ingesting a paper via read_document, check the ocr_quality field in the response:

• quality_flag == "good": proceed with extraction
• quality_flag == "degraded": warn user that some pages could not be OCR'd, suggest manual review
• quality_flag == "no_ocr_engine": suggest installing an OCR engine — pip install rapidocr_onnxruntime for local, or set VIBE_TRADING_OCR_ENGINE=llm-vision to use a vision-capable LLM model (GPT-4o, Qwen-VL, etc.) via your existing provider config
• text_density < 100: flag as potentially low-quality extraction, suggest verifying formulas manually

Phase 3: IMPLEMENT

Build the SignalEngine, run the backtest, and link results.

1. Call create_hypothesis(title, thesis, universe, signal_definition) to create a research hypothesis that tracks this work.
2. Call generate_backtest_config(hypothesis_id, start_date, end_date) to produce the config.json for the backtest runner.
3. Call scaffold_signal_engine(hypothesis_id, run_dir) to generate the skeleton signal_engine.py in the run directory.
4. Implement the full signal_engine.py using the appropriate template from templates/:
   • Factor artifacts → templates/factor_signal_engine.py
   • Strategy artifacts → templates/strategy_signal_engine.py
5. Validate syntax: bash("python -c \"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\"")
6. Call backtest(run_dir) to execute the backtest.
7. Call link_autopilot_backtest(hypothesis_id, run_dir) to link the run results back to the hypothesis.
8. Call sdm_status(action="detail", artifact_id=...) and update the artifact status to "benching".

Phase 4: EVALUATE

Judge the backtest output against quality thresholds.

1. For factors: call factor_analysis with the factor CSV and return CSV. Check:

   • IC mean > 0.03 (basic predictive power)
   • IR > 0.5 (stable effectiveness)
   • IC positive ratio > 55% (directional stability)

2. For strategies: read artifacts/metrics.csv and run_card.json. Check:

   • Sharpe ratio > 0.5 (minimum acceptable)
   • Max drawdown < 30% (risk tolerance)
   • Win rate and profit factor for additional context

3. If the artifact is alive (meets thresholds):

   • For factors: register into factors/zoo/ and update status to "active"
   • For strategies: update status to "active" with the run metrics attached

4. If the artifact is dead (fails thresholds):

   • Update status to "disabled" with a reason string
   • Record what failed (for example "IC mean 0.012, below 0.03 threshold")

5. Record bench results via sdm_status update so the history is queryable.

Phase 5: MONITOR

Track artifact health over time and handle decay.

1. Call sdm_decay_scan(universe=...) for batch monitoring across all active artifacts in a universe.
2. Review decay signals per artifact:
   • healthy: all metrics above "Healthy" thresholds (see references/decay_thresholds.md)
   • warning: metrics have dropped into the "Warning" band
   • decayed: metrics are in the "Decayed" band
   • critical: metrics are in the "Critical" band
3. Auto-transitions follow the state machine:
   • active → monitoring when any metric enters "Warning"
   • monitoring → decayed when metrics stay in "Decayed" for 3+ consecutive scans
   • decayed → disabled when metrics enter "Critical"
   • monitoring → active when metrics recover to "Healthy" for 2+ consecutive scans
4. Recovery: a decayed artifact can be re-evaluated by re-running Phase 3 with updated parameters.
5. Generate a decay report summarizing all artifact statuses and recent transitions.

Tool Reference

Tool	Phase	Purpose
read_document	1	Parse PDF papers and reports
sdm_register	2	Register extracted factor or strategy
sdm_status	2, 3, 4, 5	Query or update artifact lifecycle status
alpha_bench	2	Deduplication check against existing alphas
create_hypothesis	3	Create a research hypothesis
generate_backtest_config	3	Generate backtest config.json
scaffold_signal_engine	3	Generate SignalEngine skeleton
backtest	3	Execute the backtest
link_autopilot_backtest	3	Link backtest results to hypothesis
factor_analysis	4	IC/IR analysis for factor artifacts
sdm_decay_scan	5	Batch decay monitoring

SignalEngine Contract

The generated signal_engine.py MUST satisfy the backtest runner contract:

class SignalEngine:
    def __init__(self):
        """No-arg constructor. All parameters must have defaults."""
        ...

    def generate(self, data_map: dict[str, pd.DataFrame]) -> dict[str, pd.Series]:
        """
        Args:
            data_map: symbol -> DataFrame (columns: open, high, low, close, volume,
                      DatetimeIndex). May include extra fields from config.extra_fields
                      or config.fundamental_fields.
        Returns:
            symbol -> signal Series (float, clipped to [-1.0, 1.0])
            1.0 = fully long, 0.5 = half position, 0.0 = flat, -1.0 = fully short
        """
        ...

Hard constraints:

• Class MUST be named SignalEngine
• Constructor MUST take no arguments (all params have defaults)
• Signal Series index must align exactly with the input DataFrame index
• Include all required imports (numpy, pandas, typing)
• Do not hardcode dates or stock codes
• Do not include an if __name__ == "__main__" block
• Pure pandas/numpy implementation, no external signal libraries

Quality Checklist

Self-check before marking any phase complete:

• Paper type correctly classified (factor-research / strategy / mixed)
• Factor formula matches the paper text (no LLM hallucination)
• Deduplication check passed (IC < 0.99 against existing alphas)
• SignalEngine passes AST validation
• Backtest completed without errors
• IC/IR or Sharpe meets minimum thresholds
• Artifact registered in the strategy store with correct status

Common Pitfalls

Formula Hallucination

LLMs may generate plausible-looking formulas that do not appear in the paper. ALWAYS cross-check the extracted formula against the original document text. If the paper uses notation you cannot parse, ask the user to confirm.

Factor Deduplication

IC > 0.99 means the factor is a duplicate. IC between 0.90 and 0.99 may be a variant. Use judgment: if the formula is structurally different but produces similar signals, note it as a variant rather than rejecting it outright.

Decay Baseline

Decay monitoring requires at least 3 bench history entries to establish a baseline. A newly registered artifact with only one backtest cannot be meaningfully scanned for decay.

Template Mismatch

Strategy-type artifacts need the strategy SignalEngine template (with entry/exit/position logic), not the factor template. Using the wrong template produces a SignalEngine that compiles but generates meaningless signals.

Look-Ahead Bias

Factor values must use data from day T and earlier. Returns must use data from T+1 onward. The delta(df, d) operator enforces d >= 1 to prevent lookahead. Never use Ref(df, -n) style negative shifts.

Templates

Two SignalEngine templates are provided in templates/:

• factor_signal_engine.py: for factor-type artifacts. Computes a cross-sectional factor value per instrument per date, then ranks and clips to [-1.0, 1.0].
• strategy_signal_engine.py: for strategy-type artifacts. Implements entry/exit rules with position sizing and risk management.

References

• Decay thresholds and state machine: references/decay_thresholds.md
• Example workflows: examples.md
• Base operators for factor computation: src/factors/base.py (rank, zscore, scale, ts_mean, ts_std, ts_rank, ts_corr, ts_cov, ts_max, ts_min, ts_argmax, ts_argmin, delta, decay_linear, signed_power, safe_div, vwap)