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# Storage for Chapter 4 Baseline Runs |
This directory is the extraction target for the local data needed by the |
Chapter 4 baseline launcher. In the published repository, the reusable inputs |
are packaged as `assets/ch4_baseline_local_data.zip` and should be restored with: |
```bash |
unzip -q assets/ch4_baseline_local_data.zip -d . |
``` |
Generated run outputs, historical reports, W&B folders, and old LLM response |
caches are not kept here. |
## Kept Local Inputs |
```text |
storage/ |
├── parquet/ # Local daily price data and checksums |
├── cache/ |
│ ├── news/ # Cached raw/news-provider article files |
│ ├── news_by_day/ # Per-symbol daily news files used during backtests |
│ ├── financials/ # Cached fundamental data |
│ ├── corporate_actions/ # Dividends and split data |
│ └── stock_indicators/ # Cached technical indicator files |
└── reports/ # Empty placeholder; new runs write outputs here |
``` |
Current file counts after cleanup: |
```text |
parquet: 13440 |
cache/news: 5585 |
cache/news_by_day: 6010 |
cache/financials: 63 |
cache/corporate_actions: 42 |
cache/stock_indicators: 6783 |
``` |
## Not Kept |
- `cache/llm/`: old LLM responses from previous runs. |
- `reports/backtest/`: old generated backtest outputs. |
- `wandb/`: local W&B run artifacts. |
New executions of `run.sh` or `launch.sh` may recreate output directories as |
needed. Those outputs are run artifacts rather than baseline inputs. |
[{"cash_amount": 0.26, "currency": "USD", "declaration_date": "2025-07-31", "dividend_type": "CD", "ex_dividend_date": "2025-08-11", "frequency": 4, "id": "Ed2c9da60abda1e3f0e99a43f6465863c137b671e1f5cd3f833d1fcb4f4eb27fe", "pay_date": "2025-08-14", "record_date": "2025-08-11", "ticker": "AAPL"}, {"cash_amount": 0.26, ... |
[{"execution_date": "2020-08-31", "id": "E36416cce743c3964c5da63e1ef1626c0aece30fb47302eea5a49c0055c04e8d0", "split_from": 1, "split_to": 4, "ticker": "AAPL"}, {"execution_date": "2014-06-09", "id": "E91a6b74ca1a9dcbce26a1f34e24ae26ba2c6359822ccf901ecd827f419137654", "split_from": 1, "split_to": 7, "ticker": "AAPL"}, {... |
[{"cash_amount": 2.38, "currency": "USD", "declaration_date": "2025-08-01", "dividend_type": "CD", "ex_dividend_date": "2025-08-22", "frequency": 4, "id": "Ede25348e2df38e09ba2e8bbd85f95c31b3bcdc028ba723ee42a4a16a50e3707b", "pay_date": "2025-09-12", "record_date": "2025-08-22", "ticker": "AMGN"}, {"cash_amount": 2.38, ... |
[] |
[] |
[{"execution_date": "2022-06-06", "id": "Ef72af690fef1f9db3fd5382ca3c92c8885ea75761e9cdf54825fc7139bd88c6b", "split_from": 1, "split_to": 20, "ticker": "AMZN"}] |
[{"cash_amount": 0.82, "currency": "USD", "declaration_date": "2025-09-24", "dividend_type": "CD", "ex_dividend_date": "2025-10-10", "frequency": 4, "id": "E386ff67875eed82199fd8c537afb26c72429ae7108f92be041602d7d48584cf1", "pay_date": "2025-11-10", "record_date": "2025-10-10", "ticker": "AXP"}, {"cash_amount": 0.82, "... |
[] |
[{"cash_amount": 2.055, "currency": "USD", "declaration_date": "2019-12-16", "dividend_type": "CD", "ex_dividend_date": "2020-02-13", "frequency": 4, "id": "Ea3b40282185e0d21387578224d3bc5a326cc59b2b590ee2b6e94953a50112c44", "pay_date": "2020-03-06", "record_date": "2020-02-14", "ticker": "BA"}, {"cash_amount": 2.055, ... |
[] |
[{"cash_amount": 1.51, "currency": "USD", "declaration_date": "2025-06-11", "dividend_type": "CD", "ex_dividend_date": "2025-07-21", "frequency": 4, "id": "E3d6643c32e3b5cb4bb0d383d003591b73570a0e61f00a1fc576a7cdcb23f4a61", "pay_date": "2025-08-20", "record_date": "2025-07-21", "ticker": "CAT"}, {"cash_amount": 1.41, "... |
[{"execution_date": "2005-07-14", "id": "E909c2257331a0e29eedb1cfe6cbaf02b3c26afe5bed51a6dcd249c695b7d5c47", "split_from": 1, "split_to": 2, "ticker": "CAT"}] |
[{"cash_amount": 0.416, "currency": "USD", "declaration_date": "2025-09-04", "dividend_type": "CD", "ex_dividend_date": "2025-09-17", "frequency": 4, "id": "E82c4ff156ea2560fceb854dac1f78ea4e83b4f12dba68e9b3d1f5dfd4001cfe3", "pay_date": "2025-10-09", "record_date": "2025-09-17", "ticker": "CRM"}, {"cash_amount": 0.416,... |
[{"execution_date": "2013-04-18", "id": "Efa28b420bae18ca09bce397cb4e793feb15ffa539ba44311c5baf9c36308f657", "split_from": 1, "split_to": 4, "ticker": "CRM"}] |
[{"cash_amount": 4, "currency": "USD", "declaration_date": "2025-07-14", "dividend_type": "CD", "ex_dividend_date": "2025-08-29", "frequency": 4, "id": "E8c774e54799582e7194438a0d008779d33d0ea2dc3046d6d91e46ff0df298b0f", "pay_date": "2025-09-29", "record_date": "2025-08-29", "ticker": "GS"}, {"cash_amount": 3, "currenc... |
[] |
[{"cash_amount": 2.3, "currency": "USD", "declaration_date": "2025-08-21", "dividend_type": "CD", "ex_dividend_date": "2025-09-04", "frequency": 4, "id": "E77abc6b05cf607904c6a572c89466bb8f88c789042254384a9d0a362399c41f4", "pay_date": "2025-09-18", "record_date": "2025-09-04", "ticker": "HD"}, {"cash_amount": 2.3, "cur... |
[] |
End of preview.
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Check out the documentation for more information.
Chapter 4 Baseline Codebase
This repository is a cleaned baseline-only snapshot for the ten runs used in Chapter 4 of the thesis.
It intentionally excludes the later improvement experiments: discrete target states, Top-K shortlist, frequency experiments, simulated improvement results, and majority voting.
Contents
- Baseline source code exported from commit
3c05c1afb50afa4734296fce18cc77c776f79c1d. - The ten historical Chapter 4 W&B ids and setup metadata in
scripts/baseline_runs.json. - A single-run entrypoint:
run.sh. - A ten-run launcher with the same baseline setup as the selected original
runs:
launch.sh. - Local reusable market/news input data packaged as
assets/ch4_baseline_local_data.zip.
The raw local data directories are ignored by git after extraction:
stockbench/storage/parquet/
stockbench/storage/cache/news/
stockbench/storage/cache/news_by_day/
stockbench/storage/cache/financials/
stockbench/storage/cache/corporate_actions/
stockbench/storage/cache/stock_indicators/
Old LLM response caches, generated backtest reports, and local W&B outputs are not included.
Setup
Clone the Hugging Face dataset repository:
git clone https://huggingface.co/datasets/stock-agent/chapter4-baseline-clean
cd chapter4-baseline-clean
Restore local price/news data:
unzip -q assets/ch4_baseline_local_data.zip -d .
Data archive checksum:
sha256 3fb0ccdf05aacfeef15c032aa62b956aa6a22a247a0b56145e54b30ea7ce6dbf
Create the conda environment:
conda env create -f environment.yml
conda activate stockagent
Configure the LLM API key. The original runs used the qingyuntop
OpenAI-compatible profile. No API key is included in this repository.
export QINGYUNTOP_API_KEY="your key"
Optional W&B configuration:
export WANDB_MODE=offline # default
# export WANDB_MODE=online # upload new runs to W&B
Run
Run one baseline:
MODEL=deepseek-v3.1 RUN_PREFIX=DEEPSEEK_V3_1 ./run.sh
Run the ten Chapter 4 baseline setups:
./launch.sh
By default, run.sh uses:
START=2025-03-01
END=2026-02-28
PROFILE=qingyuntop
NEWS_ENABLED=true
DATA_MODE=offline_only
CACHE_MODE=llm_write_only
WANDB_MODE=offline
Reproduction Notes
The manifest records the original W&B ids and original code commit for each
selected run. launch.sh does not force those old W&B ids; it runs the same
baseline setup and lets the current run generate fresh timestamped ids.
The packaged local input data contains:
parquet: 13440 files
cache/news: 5585 files
cache/news_by_day: 6010 files
cache/financials: 63 files
cache/corporate_actions: 42 files
cache/stock_indicators: 6783 files
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