ArjunDivecha/visual-factor-timing · openwiki

Bundle OKF 0.1 · 9 conceitos · ArjunDivecha/visual-factor-timing

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# Files

- [Architecture](architecture.md) - High-level architecture of the factor_timing data-to-backtest pipeline, with a stage map and pointers to the detailed pipeline page.
- [Domain concepts](domains.md) - Core domain objects in factor_timing — panels, factors, monthly OHLC labels, the JKX and MXX input encodings, targets and weights, model families, and the IC-weighted ensemble forecast-to-omega-to-timed-return mechanism.
- [OpenWiki quickstart](quickstart.md) - Entry point for the visual-factor-timing knowledge base, with a compact task-routing table and links to architecture, workflows, domain, and operations pages.
- [Testing and change guide](testing-and-change-guide.md) - No automated test suite exists for factor_timing; this page lists what to verify per area, practical smoke-test commands, and the narrow validation that proves each change without a full sweep.
- [Workflows](workflows.md) - Canonical end-to-end workflows for factor_timing, from building monthly data and ca

Details

Kind: OKF bundles
Topic: Developer tools
Publisher: arjundivecha
Origin: okf_github
Category: dados
Version: 0.1
Last push: 2026-08-13T09:24:23Z
Repository state: ativo
Language: Python
Added: 2026-09-08 22:13:12
Updated: 2026-09-08 22:13:12
Origin id: ArjunDivecha/visual-factor-timing:openwiki/index.md

README

# visual-factor-timing

Replication of **Jia, Li, Zhang & Zhao (2025), "Timing the Factor Zoo via Deep Visualization"** applied to the T2 and GDELT factor panels.

## Approach
Per-factor historical return trajectories are rendered as two complementary images — a JKX-style OHLC candlestick chart with a moving-average overlay and a monthly realized-volatility strip, and an MXX-style cumulative-return bar chart — then processed by CNN and CNN-LSTM networks to produce factor-specific timing weights.

## Data sources
External xlsx files live outside the repo:
- **T2 daily**   — `A Complete/T2 Factor Timing Fuzzy Daily/T2_Optimizer.xlsx`
- **T2 monthly** — `A Complete/T2 Factor Timing Fuzzy/T2_Optimizer.xlsx`
- **GDELT daily**   — `A Complete/GDELT Factor Timing Fuzzy Daily/T2-Factor-Timing-Daily/GDELT_Optimizer.xlsx`
- **GDELT monthly** — `A Complete/T2 GDELT/GDELT_Optimizer.xlsx`

Daily returns are stored in percent; the loader rescales before cumulating.

## Pipeline
```
factor_timing/
  data/factor_loader.py      daily returns → synthetic price → monthly OHLC + vol parquet
  imaging/build_cache.py     monthly OHLC → JKX image memmap cache (window=12 or 24)
  (planned)
  imaging/mxx_renderer.py    cumulative-return trajectory (1D CNN input)
  models/cnn.py              1D CNN (64 filters, kernel 5, SiLU, dropout 0.25)
  models/cnn_lstm.py         CNN + LSTM(64) variant
  train/targets.py           raw, σ-standardized, Φ⁻¹, percentile targets; EW/EWPM weighting
  train/ensemble.py          64-model IC-weighted ensemble
  train/clustering.py        expanding-window Ward k=8 clustering for GDELT pooled training
  backtest/factor_timing.py  IC tables, α tables, OOS R², HML quintile sort
  backtest/mechanism.py      LTA, risk aversion, macro PC analysis
```

## Build

```bash
# T2
python -m factor_timing.data.factor_loader --panel t2
python -m factor_timing.imaging.build_cache --panel t2 --window 12
python -m factor_timing.imaging.build_cache --panel t2 --window 24

# GDELT
python -m factor_timing.data.factor_loader --panel gdelt
python -m factor_timing.imaging.build_cache --panel gdelt --window 12
python -m factor_timing.imaging.build_cache --panel gdelt --window 24
```

## Training strategy
- **T2** (83 factors, ~270 months each): per-factor training per the paper.
- **GDELT** (86 factors, ~129 months each): pooled training in 8 groups discovered by expanding-window Ward clustering on the factor return correlation matrix — no forward leakage since the clustering is refit at each retrain date using only data available at that point.

## Reference
Jia, Y., Li, J., Zhang, H., & Zhao, J. (2025). Timing the Factor Zoo via Deep Visualization. *Working paper, March 24, 2025.*

ArjunDivecha/visual-factor-timing · openwiki

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