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dsh-quant-workspace

Verified · install-tested on dsh AllenCX

✓ Actively maintained Builds on 4 official DSH packages

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PythonLanguage
2026-08-14Last push
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What it does

DSH plugin bridging a local low-frequent-quant engine: single-ticker signal card, backtest, review (read-only).

✅
Our take
Works — verified, early-stage project

DSH plugin bridging a local low-frequent-quant engine: single-ticker signal card, backtest, review (read-only). It installs cleanly and boots without issues in our testing. It's early-stage but functional.

“Verified” means our automated CI actually ran dsh plugin add in a clean profile and it booted — nothing more. Feature descriptions and version compatibility are the author’s claims. This is not a security audit and not an endorsement of third-party code.

README

dsh-quant-workspace

English | 中文

A self-contained quant research workspace for DeepSeek Harness.
It ships a Python engine inside the package — fetch Yahoo Finance daily data, backtest
rule-based strategies, and generate interactive visual reports, all from chat.

⚠️ Not investment advice. The workspace surfaces rule state and evidence; decisions are
always yours. It never places orders and never changes positions.

Features

  • Data — Yahoo Finance daily bars (2 years by default), with OHLCV + indicator export.
  • Backtesting — per-trade table, total return, max drawdown, win rate, average hold, and a
    buy-and-hold baseline.
  • Visual reports — self-contained interactive HTML charts: candlesticks with bands and
    entry/exit markers, volume, %B, and an equity curve. Zoom (anchored at the cursor), pan,
    crosshair, and a red-up/green-down toggle — no dependencies, open in any browser.
  • Strategy registry — save strategies you have researched and reuse them by id.
  • Read-only by design — no orders, no position changes, no market-data keys.

Requirements

  • A DeepSeek Harness installation (web profile) with pnpm.
  • uv (runs the bundled Python engine; first use syncs python/.venv).
  • Internet access for Yahoo Finance data.

Install

Installation status: not yet published to npm. Until then, install from the git spec
(dsh plugin --profile web add github:AllenCX/dsh-quant-workspace) or use the dev overlay below.

dsh plugin --profile web add dsh-quant-workspace

All configuration is optional. Example user patch ($DSH_HOME/profiles/web/cordis.patch.yml):

- id: quant-workspace
  config:
    ledgerPath: 'C:\path\to\trade_log.csv'   # optional: track your real positions
    reportsDir: 'C:\path\to\reports'          # optional: where visual reports go
Option Default Meaning
ledgerPath (none) Position ledger CSV (date,ticker,action,price; FIFO). Positions are only tracked from this file.
reportsDir $DSH_HOME/dsh-quant-workspace/reports Directory for visual report artifacts (HTML charts) and state exports.
registryPath $DSH_HOME/dsh-quant-workspace/strategies.json Strategy registry JSON file.
defaultRule (none) Default rule family used when a call does not specify one.
timeoutMs 180000 Foreground timeout per tool call.
pythonCommand uv run --project <package>/python dsh-quant Override for running the bundled engine CLI (e.g. a pre-built venv).

Dev / local overlay

pnpm dsh web --patch ./dev.patch.yml

Quick start

In a Harness session:

  • "Give me today's signal card for TSLA" — single_ticker, mode daily.
  • "Backtest META and generate a chart" — single_ticker, mode backtest, chart: true.
  • "Compare the review health check for TSLA" — single_ticker, mode review.

A call runs exactly one rule: an example rule family, a registered strategy id, or the
configured defaultRule when neither is given. Without any of these, the workspace reports
that no strategy is selected.

Tools

single_ticker

  • ticker (required) — symbol, e.g. TSLA. Uppercased automatically; only letters, digits, dot and dash.
  • mode (default daily) — daily signal card · backtest with per-trade table · review health check.
  • rule — an example rule family (currently bollinger_mean_reversion).
  • strategy — id of a strategy in the workspace registry.
  • chart (default false) — also write an interactive HTML report and the state CSV under reportsDir;
    the artifact paths are included in the output.

Strategy registry

After research, save a strategy and reuse it by id:

dsh-quant strategy register --id tsla_dip --family bollinger_mean_reversion --bollinger-window 30 --note 'dip strategy after Aug-2026 research'
dsh-quant strategy list
dsh-quant strategy remove --id tsla_dip

Example rule

The bundled engine ships one example rule so the workspace works out of the box: Bollinger
mean-reversion on daily bars — enter when %B <= 0, exit when %B >= 1 (Bollinger 20, 2σ,
same-bar close fills, no transaction costs in v1). Rule parameters are CLI options, and more
rule families (MA cross, Donchian, RSI, trend filters) are on the roadmap.

CLI reference

dsh-quant single-ticker --ticker <T> --mode <daily|backtest|review> (--rule <family> | --strategy <id>) [--ledger <path>] [--chart <dir>] [--export-state <dir>] [--registry <path>] [--data-file <csv>]
dsh-quant strategy register|list|remove [options]
  • Exit 0 with plain-text report on success; exit 1 when data cannot be loaded; exit 2 for invalid invocation.
  • --data-file reads a local OHLCV CSV instead of the network (used by the tests).

Development

pnpm install && pnpm run typecheck && pnpm run test && pnpm run build   # TS shell
cd python && uv run --project . pytest tests -q                          # bundled engine

License

MIT

Install

🧩 Let your agent install it (recommended)

Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:

dsh plugin add dshbase-catalog

Then say "install dsh-quant-workspace for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.

This plugin is GitHub source (not published to npm) — install it straight from the repo:

Web profile:

dsh plugin --profile web add github:AllenCX/dsh-quant-workspace

Headless (CLI) profile:

dsh plugin --profile headless add github:AllenCX/dsh-quant-workspace

Test report

Verified: L1 install + L2 load + L3 runtime from GitHub source on dsh 0.1.0-rc.6.

When to use it

Let the agent crunch data — parse, transform, or analyze — so it works with real datasets, not just prose.

Who it's for

Users whose tasks involve tables, files, or numbers that need processing or analysis.

For developers — extending it

Data sources and transformation tools are the seams — add formats, aggregations, or visualization outputs.

Security: not yet scanned — our daily static scan will cover it shortly.

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