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Flagship Strategy Blueprint (Sample Edition)

The flagship package now ships with a reproducible cross-sectional momentum pipeline that demonstrates the full microstructure stack—configurable slippage, borrow accrual, limit-order queueing, covariance allocators, and bootstrap reporting.

Dataset

  • Location: data/sample/
  • Files:
  • prices_sample.csv: panel of six synthetic equities (~3y of business days).
  • prices/: per-symbol CSVs consumed by MultiCsvDataHandler.
  • meta_sample.csv: ADV, borrow cost, and spread inputs for slippage/queue modelling.
  • rf_sample.csv: daily risk-free rate in basis points.
  • universe_sample.csv: monthly eligibility snapshot with sector and liquidity stats.

No external data vendors are required; runs are deterministic.

Pipeline Overview

Component Implementation
Signals 12-1 sector-neutral momentum (FlagshipMomentumStrategy)
Allocation Budgeted risk parity with Ledoit–Wolf fallback (microalpha.allocators)
Execution TWAP with IOC queue model, linear+sqrt impact with spread floor
Financing Daily borrow accrual from meta_sample.csv
Risk Controls Sector caps, exposure heat, ADV turnover clamp
Evaluation HAC-adjusted Sharpe, Politis–White bootstrap (stationary blocks)

Reproduce the Single-Run Case Study

make dev          # optional helper -> pip install -e '.[dev]'
microalpha run --config configs/flagship_sample.yaml --out artifacts/sample_flagship
microalpha report --artifact-dir artifacts/sample_flagship
  • Outputs metrics.json, bootstrap.json, exposures.csv, trades.jsonl, and tearsheet.png.
  • reports/summaries/flagship_mom.md is refreshed automatically by the report step.

Walk-Forward Reality Check

microalpha wfv --config configs/wfv_flagship_sample.yaml --out artifacts/sample_wfv
microalpha report --artifact-dir artifacts/sample_wfv --summary-out reports/summaries/flagship_mom_wfv.md --title \"Flagship Walk-Forward\"
  • Sliding window: 252-day train / 63-day test.
  • Grid: {top_frac ∈ {0.3, 0.4}, skip_months ∈ {1, 2}}.
  • Stores per-fold metrics, queue-aware execution logs, and bootstrap Sharpe distributions.

Key Metrics (generated)

Results will vary slightly with config tweaks; the shipped summary documents the canonical run and includes:

  • Sharpe ratio with HAC standard errors.
  • Calmar / Sortino / turnover.
  • Bootstrap Sharpe histogram + p-value.
  • Top absolute exposure table driven by final holdings.

Extending Beyond the Sample

  1. Swap data_path to a directory of per-symbol CSVs (format identical to data/sample/prices/*.csv).
  2. Update meta_path with symbol-specific ADV/borrow/spread estimates.
  3. Adjust allocator settings via strategy.allocator / allocator_kwargs in the config.
  4. Tune queue parameters under exec.queue_* for different liquidity regimes.

The accompanying tests (tests/test_flagship_momentum.py, tests/test_allocators.py, tests/test_reality_check_store.py) codify invariants for momentum selection, covariance allocation, and bootstrap artefact persistence.