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pengpengyi92/dsh-quant

插件Plugin 原生Native ⭐ 23 MIT cat_label.ai-agentscat_label.ai-agentscat_label.algorithmic-tradingcat_label.algorithmic-tradingcat_label.backtestingcat_label.backtestingcat_label.deepseekcat_label.deepseekcat_label.deepseek-harnesscat_label.deepseek-harnesscat_label.dshcat_label.dsh

🐳 Dsh-Quant：全能型AI原生量化操作系统

项目介绍Project Overview

由知微Agent(zhiwei)编辑Edited by Zhiwei Agent (zhiwei)

dsh-quant 是一款面向 DSH 的 AI 原生量化插件，内置 59 个工具覆盖数据、因子、ML、风控、执行与生态六大域，提供 PDAT→PET 一体化研究管线。所有数值方法均为零依赖纯函数，215 个单元测试离线可验。适合需要让 Agent 自动编排数据接入、指标计算、回测与组合分析的量化研究者。需注意：实时行情相关工具依赖网络，离线仅覆盖指标与回测。

dsh-quant is an AI-native DSH plugin exposing 59 tools across data, alpha, ML, risk, execution, and ecosystem domains, forming an end-to-end PDAT→PET research pipeline. All numeric methods are zero-dependency pure functions with 215 offline-verifiable unit tests. Use it when an agent must autonomously orchestrate data ingestion, indicators, backtests, and portfolio analytics. Caveat: live market tools require network access; offline mode covers indicators and backtests only.

在 GitHub 查看View on GitHub

强烈推荐Recommended 发给你的 DSH 安装Send to your DSH 
请帮我了解并安装插件：【dsh-quant】【https://github.com/pengpengyi92/dsh-quant】

把上面这条消息直接发给当前会话里的 DSH，让它帮你了解并安装。安装命令不一定准确，发给 DSH 更稳。Send this message to DSH in your current session. CLI install commands may not be accurate across systems — DSH will figure it out for you.

或使用命令行安装（适合开发者）Or use CLI install (for developers)

命令行安装CLI Install 

dsh plugin --profile web add github:pengpengyi92/dsh-quant

把 pengpengyi92/dsh-quant 加入你的 DSH 配置（web profile）即可启用。

READMEREADME

37.4 KB · 缓存于 2026-08-26 05:39:31 · 37.4 KB

🐳 dsh-quant — The Everything-Plugin Quant OS

🌐 Site: https://dsh-quant-site.pages.dev · ✅ Listed in awesome-dsh-plugin (one-click install via dsh-market)

npm downloads stars site license ci dsh-plugin

AI-native & DSH-native quant toolkit for every quant aspect — 59 tools · 6 domains (data / alpha / ML / risk / execution / ecosystem) · one end-to-end PDAT→PET research pipeline. Methods open, secrets internal.

🧩 Core Philosophy: Everything is a Plugin (quant edition)

dsh's philosophy is everything is a plugin; dsh-quant brings it to quant — open-sourcing the internal five-team paradigm (PDAT → PAAT → PCPT → PRT → PET) as five pluggable modules:

data plugin   dsh-data      market data / sources / quality  ← plug in Binance or your own data
alpha plugin  dsh-alpha     indicators / factors / eval      ← write your own alpha (internal alpha stays private)
model plugin  dsh-ml        backtests / ML/DL/RL framework   ← train your own models (internal research stays private)
risk plugin   dsh-risk      VaR / drawdown / options / bonds ← set your own risk limits
exec plugin   dsh-execution sim execution / fund / report    ← build your own trading system (paper or live)
  • What's open is the paradigm: how modules compose, how contracts are defined (null alignment / no look-ahead / hand-computed tests), how results are validated — not the internal secrets
  • You fill it in: product power = UI + strategies + data interfaces + DL/RL models + trading-system building, all self-assembled, all pluginized
  • Infinite self-evolution: fill the framework with your modules → run paper/live → feed the ecosystem back — that's dsh-quant

Plugin call for proposals: Issue #27 (five modules × many plugins) — PDAT plugins, PET plugins, anything you can imagine 🐋

🤖 AI-Native Is Deliberate (design statement)

dsh-quant's primary consumer is the agent (the model), not the human — a deliberate choice from day one:

  • Tool schemas are injected into the system prompt — every contract (args / outputs / alignment rules) is written from the model's perspective
  • Equal-length null alignment — outputs match input length; leading window positions are null, so the model aligns by index and never pads
  • Canonical JSON + render separation — machines read structure, humans read prose
  • All isConcurrencySafe — pure functions, no shared state; agents can call all 59 tools in parallel without interference
  • Skill layer — skill/quant-research lets the model load the workflow itself

Full statement: Issue #14 "AI-native is deliberate".

🐍 Why Almost No Python?

A common question: a quant project without .py files? Answer: 0 Python files, 21 TypeScript source files, zero runtime dependencies — deliberate:

  • dsh-quant is a dsh plugin running inside the harness's Node runtime: same process as the agent, composable by the Loader, reversible registration (HMR-safe) — TypeScript is the only natural choice
  • Where Python is needed (akshare / tushare / baostock), use the channel knowledge base (quant_data_guide, 15 channels): dsh ships shell / subprocess capabilities, so the agent decides whether to spawn Python — dsh-quant carries no runtime
  • All numerical methods (indicators / backtests / options / bonds) are pure functions with hand-computed baselines: zero deps, offline-verifiable, npm test green anywhere

One goal: build the most usable AI-native quant repo 🐋

59 quant_* tools · 6 domains · 215 unit tests · zero runtime deps. Full positioning: pinned Issue #9.

🤖 For visiting agents

Agents landing on this repo: start at AGENTS.md (root-level, auto-read by Copilot / Claude Code / dsh), then the closed-loop example plugin/CASE_STUDY.md or Discussion #38, and the one-glance contract mcp/AGENT_GUIDE.md.

Quick Install (dsh users)

npm i dsh-quant

Add one line to your cordis.yml:

- name: 'dsh-quant'

59 tools auto-register — indicators / backtests / factors / risk / fund simulation / ecosystem metrics out of the box. One quant_research_pipeline runs the whole PDAT→PET chain. ML/DL knowledge: docs/ML_GUIDE.md; executable demo: npx tsx demos/ml-workflow.ts.

🚀 Product Experience: Three Minutes to a Full Quant Pipeline

Right after install, experience the complete PDAT→PET flow (BTC public data + simple strategy + backtest + paper trading):

data(quant_market_fetch) → quality(quant_data_quality) → factors(quant_factor_evaluate)
→ backtest(quant_backtest) → metrics(quant_metrics) → risk(quant_risk)
→ drawdown(quant_drawdown) → paper sim(quant_execute_sim) → fund sim(quant_fund)
→ report(quant_report)

One-liner: quant_research_pipeline(symbol=BTCUSDT, limit=120) returns everything in one call.

Then plug your own plugins into each module (data sources / alpha / models / risk / execution — everything is a plugin, proposals at Issue #27).

Five-step walkthrough with commentary: docs/ONBOARDING.md · Agent one-glance guide: mcp/AGENT_GUIDE.md

🖥️ UI Workbench (dsh-quant-ui)

dsh-quant UI

dsh-quant-ui: candlesticks + MA overlays + trade markers, equity curves, fund NAV / management-fee / performance-fee cards, metric selector — plus a swimming chibi whale 🐋 (click the title 3 times).

Live demo: https://dsh-quant-ui.pages.dev

⌨️ CLI (dsh-quant terminal)

Zero-dependency readable terminal (pure Node + ANSI, same philosophy as the P-Research CLI). Browse the research columns and live market data without a browser:

node cli/main.mjs repo                      # 59 tools · 6 domains
node cli/main.mjs history                   # 53 firm archives index
node cli/main.mjs history citadel           # one firm's archive (rendered)
node cli/main.mjs history --reports         # ANALYSIS / TIMELINE / LINEAGE / BANK_LINEAGE
node cli/main.mjs history --search 高频      # cross-archive search
node cli/main.mjs kline BTCUSDT --limit 20  # colored OHLC table + stats
node cli/main.mjs browse                   # interactive TUI: arrow-key firm browser

After npm install -g ., the commands shorten to dsh-quant repo, dsh-quant history citadel, etc.

Tools

Tool Parameters Canonical output First valid index
quant_data_compare dataType (e.g. "financials"/"daily bars") { dataType, channels: [{ name, cost, covers, bestFor }] } (covering first) —
quant_data_advice dataType + budget (free/low/institutional) + purpose (research/backtest/official) { recommendations: [{ rank, name, reason }] } (decision-tree ranked) —
quant_series_stats values: number[] { count, mean, std, min, max, median, skew, kurtosis, autocorr1, annualizedVol, totalReturnPct } — (first step after fetching)
quant_var_backtest returns + varSeries + confidence=0.95 { failures, expected, lrStat, pValue, passed, periods } (Kupiec POF test) — (the ground truth for VaR models)
quant_option spot + strike + timeToMaturity + riskFreeRate + type + exactly one of volatility/price { price, impliedVolatility, delta, gamma, vega, theta, rho, … } — (Optiver-inspired: BS pricing + five greeks + IV solve)
quant_volatility close: number[] + annualization=252 { annualized, perPeriod, n, logReturns(aligned) } — (realized vol; the RV-vs-IV research entry)
quant_bond couponRate + periodsToMaturity + paymentsPerYear? + exactly one of ytm/price { price, yieldToMaturity, macaulayDuration, modifiedDuration, convexity, dv01, … } — (FICC link: pricing/duration/convexity/DV01, textbook discounting)
quant_drawdown equity: number[] { underwater(aligned), maxDrawdownPct, currentDrawdownPct, periods(peak/trough/recovery/depth/duration), ongoing } — (drawdown episode analysis)
quant_resample candles + period (week=7 bars/month=30 bars) { candles } (OHLCV aggregation, 24/7 markets) —
quant_report strategy/metrics/risk/factor/fund (module outputs) { report } (Markdown research report) — (R&D conclusion assembly)
quant_repo_stats owner + repo { stars, forks, watchers, openIssues, openPullRequests, topics, latestRelease, … } (public GitHub API, no credentials) — (ecosystem data)
quant_npm_stats pkg { latest, weeklyDownloads, monthlyDownloads, description, … } (npm registry + downloads API) — (ecosystem data)
quant_oss_pulse stars + downloadsWeekly? + starsPrevious? + openIssues? + openPullRequests? + daysSinceRelease? { score(0-100), grade(A-D), components, suggestions, summary } — (open-source influence score; missing inputs score neutral 50)
quant_stress_test weights + betas + assetVolsPct + correlation=0.6 { weights, scenarioLossesPct, worstScenario, maxLossPct, portfolioVolPct, notes } — (portfolio loss under crash/liquidity/vol scenarios)
quant_risk returns (decimal series) + benchmarkReturns? + confidence=0.95 { var95, cvar95, downsideDeviation, maxDrawdownPct, beta, alpha, informationRatio, trackingError, periods } — (core risk module)
quant_fund equityCurve + initialCapital=1e8 + managementFeeRate=0.02 + performanceFeeRate=0.2 { initialCapital, finalNavNet, finalAum, peakNav, peakAum, gross/netReturnPct, fees, navNet } — (quant hedge-fund sim: NAV 1.00 start, daily mgmt fee, 20% high-water-mark performance fee)
quant_metrics equityCurve + trades? { totalReturnPct, maxDrawdownPct, sharpe, annualizedVol, calmar, sortino, winRate, profitFactor, avgPeriodReturnPct, tradeMetrics } (required trio: return/drawdown/sharpe) — (METRIC_CATALOG for UI pickers)
quant_chart kind (candles/series/annotations) + matching data structured chart data (dsh-chart protocol: candles+overlays+markers / multi-series / annotation views) — (UI-route data plane)
quant_execute_sim close + orders[{index, side, quantity?/valueFraction?}] + initialCash? + feeRate? + slippageBps? + latencyBars? { fills, equityCurve, finalEquity, totalReturnPct, totalFee, totalSlippageCost, tradeCount, unfilledCount, cash, position } — (execution framework, no live trading)
quant_trading_cost quantity + price + commissionRate=0.001 + spreadBps=5 + annualVolPct=30 + dailyAdv? + participationRate=0.01 { totalCostBps, commissionBps, slippageBps, impactBps, notional, notes } — (commission + slippage + market impact)
quant_trade_quality fills (from quant_execute_sim) + unfilledOrders? + holdingPeriodBars? { orders, fills, fillRate, totalSlippageCost, avgSlippageBps, avgHoldingBars, buys, sells, avgFillValue, notes } — (execution quality: sim → live expectations)
quant_research_pipeline symbol? + interval? + limit? + provider? + candles? + strategy/fund params { candles, quality, stats, metrics, risk, drawdown, fund, factor, report, charts } — (one-call PDAT→PET research)
quant_factor_evaluate factorValues + forwardReturns (factor[i] predicts ret[i+1]) + quantiles=5 + window=20 + decayHorizons=5 { ic, rankIc, icDecay, icir, icSeries, quantileReturns, longShort, turnover, autocorr1, n } (alphalens set + RankIC/IC decay) —
quant_factor_neutralize factorValues + groups? + styleFactors? + method? { values(standardized), method, groupCount, styleCount, rSquared } — (group z-score / OLS residual neutralization)
quant_walk_forward returns + features[][] + trainWindow + testWindow + step? { predictions(null-aligned), oosIc, oosRankIc, oosCount, windows, trainR2Mean } — (rolling train / out-of-sample, no look-ahead)
quant_rebalance_schedule driftPerPeriod + costPerRebalance + maxFrequency=60 { frequencies, totalCosts, bestFrequency, bestCost, costBreakdown, notes } — (drift vs cost: optimal rebalance frequency)
quant_parameter_sensitivity baseValue + range=0.2 + steps=9 + metricValues? { paramName, values, metricValues, baseValue, robustness, bestValue, bestMetric, worstMetric, notes } — (grid robustness: plateau vs needle-sharp)
quant_linear_model X(samples×features) + y + lambda? + predictX? + yTest? { intercept, weights, lambda, trainR2, n, predictions?, testR2?, testIc? } — (standalone OLS/Ridge fit & predict)
quant_factor_correlation factors (equal length) + factorNames? + threshold=0.7 { factorNames, correlationMatrix, highCorrelationPairs, meanAbsCorrelation, effectiveFactorCount, notes } — (factor redundancy: dedupe before combine)
quant_factor_combine factors: number[][] (equal length) + weights? { signal(rank 0..1), effectiveWeights, factorCount } — (z-score weighting + cross-sectional ranking)
quant_ic_decay factor + returns (same length) + maxHorizon=10 { horizons, icByHorizon, halfLife, bestHorizon, peakIc, peakHorizon, signalType, notes } — (IC decay: signal shelf-life → rebalance frequency)
quant_layered_backtest factor + returns (time×asset matrices) + layers=5 + horizon=5 + feeRate=0.001 { layers, topEquity, bottomEquity, longShortEquity, topReturnPct, bottomReturnPct, longShortReturnPct, rebalances, layerMeanReturnPct, notes } — (quantile-layer backtest: factor → strategy sketch)
quant_series_quality values: number[], jumpThreshold=0.2 { count, missingCount, zOutliers, jumps, longestConstantRun, healthy } — (series-level quality)
quant_data_annotate values: number[], jumpThreshold=0.2 { count, annotations: [{index, label, severity, detail}], summary } — (point-level labeling, a tribute to Scale AI)
quant_data_quality candles (quant_market_fetch output) { count, highBelowLow, nonPositive, timeNotIncreasing, timeGaps, extremeMoves, healthy } — (pre-analysis health check)
quant_deflated_sharpe observedSharpe + numPeriods + numTrials=1 + skewness? + kurtosis? { observedSharpe, minSignificantSharpe, deflatedSharpe, significant, pValue, notes } — (Bailey & López de Prado overfitting-adjusted Sharpe)
quant_data_pit values: (number|null)[] + channels? { healthScore, pit{pass, lookAheadIndices, notes}, survivorship{continuous, gaps, tailTruncated}, channels[] } — (AI-infra quality: point-in-time / survivorship / channel reliability)
quant_channel_guide channel (e.g. "akshare") + check? + hasCredentials? { channel, displayName, steps[], prerequisites[], example, fallback, readiness? } — (agent-ready channel access guide + readiness check)
quant_data_guide query (channel name/data type, e.g. "tushare"/"financials") or channel (exact name) { query, results: [{ name, url, cost, dataTypes, setup, tutorialUrls, bestFor, … }] } — (built-in 15-channel data knowledge base: A-shares/US/bonds + dsh ecosystem data plugins)
quant_market_fetch symbol: string (e.g. BTCUSDT / sh600000 / AAPL), interval: 1m…1M, limit: 1-1000, provider: binance/okx/bybit/sina/tencent/yahoo { symbol, interval, provider, candles: [{openTime, open, high, low, close, volume}] } —
quant_sma values: number[], window: integer { values: (number|null)[], window } index window-1
quant_ema values: number[], window: integer { values: (number|null)[], window } index window-1 (seed = first-window mean, alpha = 2/(w+1))
quant_rsi values: number[], window: integer = 14 { values: (number|null)[], window } index window (Wilder smoothing)
quant_macd values: number[], fast=12, slow=26, signal=9 { macd, signal, histogram } (equal length) macd: slow-1; signal/histogram: slow+signal-2
quant_bollinger values: number[], window=20, multiplier=2 { upper, middle, lower, window, multiplier } index window-1 (population std)
quant_atr high/low/close: number[], window=14 { values: (number|null)[], window } index window (Wilder smoothing)
quant_kdj high/low/close: number[], window=9 { k, d, j } (equal length) index window-1 (RSV method, K/D seeded at 50)
quant_williams_r high/low/close: number[], window=14 { values: (number|null)[], window } index window-1 (range -100..0)
quant_cci high/low/close: number[], window=20 { values: (number|null)[], window } index window-1 (±100 overbought/oversold)
quant_obv close/volume: number[] { values: number[] } everywhere (first value 0, no nulls)
quant_adx high/low/close: number[], window=14 { adx, plusDi, minusDi, window } ±DI: index window; ADX: index 2*window-1
quant_roc values: number[], window=12 { values: (number|null)[], window } index window
quant_backtest close: number[], fast=10, slow=30, feeRate=0.001, stopLoss?, takeProfit? { totalReturnPct, maxDrawdownPct, sharpe, position, equityCurve, trades(with exitReason) } first trade one bar after first confirmed cross
quant_backtest_bollinger close: number[], window=20, multiplier=2, feeRate=0.001, stopLoss?, takeProfit? same (buy on upper-band breakout, sell on mid-band cross-down) one bar after first confirmed breakout
quant_backtest_rsi close: number[], rsiWindow=14, buyBelow=30, sellAbove=70, feeRate=0.001, stopLoss?, takeProfit? same (buy on RSI cross-up through buyBelow, sell on cross-down through sellAbove) one bar after first confirmed signal
quant_backtest_portfolio assets: [{name, close}], weights?, rebalanceEvery?, feeRate=0.001 { totalReturnPct, maxDrawdownPct, sharpe, equityCurve, assetNames, finalWeights, rebalances } — (multi-asset portfolio)
quant_portfolio_optimize returns (time×asset matrix) + method=maxSharpe|minVar|riskParity + iterations? { method, weights, annualReturnPct, annualVolPct, sharpe, assetSharpe, concentration } — (weight optimizer; feed result to quant_backtest_portfolio)
quant_attribution returns (time×asset) + weights (sum 1) + factorExposures? [time][asset][factor] { totalReturnPct, assetContributionsPct, assetContribShares, factorContributionsPct, residualPct, factorR2, notes } — (portfolio attribution: where did the return come from)
quant_backtest_grid close: number[], fastMin=3, fastMax=10, slowMin=10, slowMax=30, feeRate=0.001 { results(sorted by return desc), best, fastRange, slowRange, feeRate } — (grid search; skips fast >= slow)

Typical chain (model's view)

quant_market_fetch(symbol: BTCUSDT, interval: 1d, limit: 100)
  → take close → quant_sma / quant_ema / quant_rsi / quant_macd / … → quant_backtest

Verified live: real Binance daily bars → indicators → backtest (fast 5 / slow 20) end to end.

Backtest contract

  • Dual-MA crossover: buy all-in when fast SMA crosses above slow SMA, liquidate when it crosses below; signals confirm on bar i and fill at bar i+1 close (no look-ahead).
  • Fees are charged on both sides of notional (feeRate per side).
  • Open tail position: the last trade's exitIndex/exitPrice/returnPct are null.
  • position and equityCurve match input length; equity is normalized (starts at 1); Sharpe is annualized assuming daily frequency (√365).

Alignment conventions

  • All outputs are equal-length with inputs; leading unwindowed positions are null — the model aligns by index, no padding needed.
  • Empty series or window > series length is a legal result (all null), not an error.
  • Non-finite numbers (NaN/Infinity) are rejected at the registry's lossless-JSON argument snapshot layer (the model's JSON boundary) and never reach execute.
  • Constraints (window ≥ 1 integer, macd fast < slow, atr arrays equal length, multiplier > 0) are hand-checked in execute; thrown errors become isError results via the registry.

Contract (defineTool)

  • Arguments use the unified schema DSL, validated by defineTool before execute (types / required / integers).
  • execute returns only the canonical JSON value; output.render produces the model-facing prose.
  • Every tool is isConcurrencySafe: true — pure functions, no shared state, no side effects, parallel-schedulable.
  • Registration is a reversible effect: ctx.tools.register returns a disposer; fiber disposal unregisters.

Model Experience

What the model sees

Each tool's name/description/JSON schema is injected into the system-prompt assembly (ctx.systemPrompt.tools()). Descriptions state the alignment rules (which head positions are null), so the model never guesses.

Token impact

Each tool costs one fixed schema block; call results are charged by rendered content. The null-alignment design avoids repeated padding requests from the model.

KV cache impact

The schema prefix is stable (reused as long as the tool set and order are unchanged); results append after the reusable prefix.

Release history (NEWS)

Version Date Notes
0.90.0 2026-08-23 HIGHFLYER_SPECIAL — dual-engine king (quant funds AGI, DeepSeek $45B, Wenfeng world AI-richest), 50 reports total
0.89.0 2026-08-23 UBIQUANT_SPECIAL — China AI-transform deep-dive (WorldQuant lineage, IQuest-Coder 40B open source, capsizing paradigm), 49 reports total
0.88.0 2026-08-22 quant_trading_cost + quant_rebalance_schedule — cost gate + drift/cost optimizer (59 tools, 215 unit)
0.87.0 2026-08-22 Validation tools ×4 — factor_correlation / deflated_sharpe / stress_test / parameter_sensitivity (57 tools, 210 unit)
0.86.0 2026-08-22 Bridges ×3 — quant_layered_backtest / quant_trade_quality / quant_attribution (53 tools, 200 unit)
0.85.0 2026-08-22 quant_ic_decay + quant_portfolio_optimize (50 tools, 193 unit)
0.84.0 2026-08-22 AI-infra data modules ×4 — quant_data_pit / quant_channel_guide / CLI quality / chain-loop (48 tools, 186 unit)
0.83.0 2026-08-22 quant_factor_neutralize repaired + 5 baselines (46 tools, 179 unit)
0.82.0 2026-08-22 TYO_QUANT — Tokyo yen-rates-center census (~9 firms, $30M talent war, Capula stronghold), 48 reports total
0.81.0 2026-08-20 CHI_QUANT — Chicago market-making city census (~14 firms, exchange-gene, UChicago pipeline, Citadel exit), 47 reports total
0.80.0 2026-08-20 QUANT_PEOPLE_CN + QUANT_PEOPLE_GLOBAL — 101st-release quant headcount estimates (CN ~30-50k, 4-city ~25-38k, global ~80-120k), 46 reports total
0.79.0 2026-08-20 QUANT_WORLD_MAP — 100th-release special: global quant world map (5-city axis, 9 paths, talent trees, 4-city census synthesis), 44 reports total
0.78.0 2026-08-20 NYC_FOREIGN_QUANT — New York hedge-fund-universe census (~28 firms, 12 HQs, CT suburb dark core, NY-LDN twin), 43 reports total
0.77.0 2026-08-20 LDN_FOREIGN_QUANT — London global-quant-hub census (~30 firms, 12 HQs, four-city comparison), 42 reports total
0.76.0 2026-08-20 SG_FOREIGN_QUANT — Singapore foreign-quant census (~20 firms, crypto/MM/family-office edge, HK twin comparison), 41 reports total
0.75.0 2026-08-20 HK_FOREIGN_QUANT — Hong Kong foreign-quant census (~26 firms, 5 categories, hub-vs-branch, 2025-26 expansion wave), 40 reports total
0.74.0 2026-08-20 QRT_SPECIAL — data-king deep-dive (Credit Suisse MBO, $42B in 10y, Dao China 10×/98%), 39 reports total
0.73.0 2026-08-20 TWOSIGMA_SPECIAL — ML-pioneer deep-dive (DE Shaw spawn flagship, data-first, dual-founder governance crisis), 38 reports total
0.72.0 2026-08-20 DESHAW_SPECIAL — cradle-king deep-dive (computational finance origin, DE Shaw Mafia, Anton supercomputer), 37 reports total
0.71.0 2026-08-20 RENAISSANCE_SPECIAL — black-box-king deep-dive (Simons' three turns, Medallion 66%/30y, $100B+ profits), 36 reports total
0.70.0 2026-08-20 WORLDQUANT_SPECIAL — alpha-factory deep-dive (BRAIN crowdsourcing, 100M alphas, IQC, 101 Alphas), 35 reports total
0.69.0 2026-08-20 SIG_SPECIAL — poker-mother deep-dive (probability OS, ByteDance 15,000×, talent tree root), 34 reports total
0.68.0 2026-08-20 CITADEL_SPECIAL — scale-king deep-dive (dual-engine fund+market-making, $16B peak year, Miami HQ), 33 reports total
0.67.0 2026-08-19 XTX_SPECIAL — per-capita-productivity king deep-dive (£14M/head, six secrets), 32 reports total
0.66.0 2026-08-19 SHOWDOWN_CN_GLOBAL — six-dimension CN-vs-global showdown (+ transparency inversion), 31 reports total
0.65.0 2026-08-19 LISTED_QUANT — listed-quant census (Virtu/Flow/Man + Knight death chain), 30 reports total
0.64.0 2026-08-19 CAPITAL_MODEL — foreign capital-structure census (prop/fundraise/hybrid), 29 reports total
0.63.0 2026-08-19 POD_PLATFORM — pod-shop capstone (5 angles + dsh isomorphism), 28 reports total
0.62.0 2026-08-19 BALYASNY_SPECIAL — sixth firm deep-dive (Schonfeld lineage + 2018 halving + rebuild), 27 reports total
0.61.0 2026-08-19 MILLENNIUM_SPECIAL — fifth firm deep-dive (pod federation + China talent root), 26 reports total
0.60.0 2026-08-19 POINT72_SPECIAL — fourth firm deep-dive (SAC rebirth + Cubist + 14 offices), 25 reports total
0.59.0 2026-08-19 OPTIVER_SPECIAL — third firm deep-dive (Dutch name + Ready Trader Go + tool lineage), 24 reports total
0.58.0 2026-08-19 JANE_STREET_SPECIAL — second firm deep-dive (SIG trio + OCaml culture), 23 reports total
0.57.0 2026-08-19 IMC_SPECIAL — first firm deep-dive special (office chronicle + Prosperity), 22 reports total
0.56.0 2026-08-19 QUANT_VENDORS_CN — China's picks-and-shovels layer (Kafang/RQAlpha/jqdatasdk), 21 reports total
0.55.0 2026-08-19 FOREIGN_CN_MAP_V2 — fully verified foreign-in-China map (7 PFM, second wave 2024-2026), 20 reports total
0.54.0 2026-08-19 Shanghai gravity + foreign-in-China map — SHANGHAI_GRAVITY + FOREIGN_CN_MAP, 19 reports total
0.53.0 2026-08-19 Quant maps ×2 — QUANT_MAP_CN + QUANT_MAP_GLOBAL (city-centric), 17 reports total
0.52.0 2026-08-19 Office maps ×2 — OFFICE_CN + OFFICE_GLOBAL, 15 reports total
0.51.0 2026-08-19 Signature encyclopedias ×2 — SIGNATURES_CN + SIGNATURES_GLOBAL, 13 reports total
0.50.0 2026-08-19 Age chronicles ×2 — AGE_CN (2004-2022) + AGE_GLOBAL (1783-2018), 11 reports total
0.49.0 2026-08-19 D-tier research reports ×4 — REGULATION / TALENT_MAP / STAR_PRODUCTS / QUANT_AI (9 reports total)
0.48.0 2026-08-19 5 cross-border archives — Tengsheng/Inshiman/Yuansheng/GSR/Eisler (94 firms)
0.47.0 2026-08-19 8 CN Lite archives — Kaifeng/Honghu/Egret/Zhuoshi/Hande/Niankong/Mengxi/Xinhong (89 firms)
0.46.0 2026-08-19 7 CN Lite archives — Shenyi/Jasper/Liyi/Bodao/Zunjia/Qianyi/Pingfanghe (81 firms)
0.45.0 2026-08-19 3 CN Lite archives — Tianyan/Aifang/Maoyuan (74 firms)
0.44.0 2026-08-19 10 CN Lite archives — Zhicheng/Qianxiang/Blackwing/Inno/LongQi/JoinQuant/Evolution/Sixie/Bridgewater-CN/Beyang (71 firms)
0.43.0 2026-08-19 Golden Bull special — 12 years of quant winners (2014-2025) + archive cross-analysis
0.42.0 2026-08-19 5 Lite archives — Hongxi/Mingshi/Wenbo/Luoshu/Pansong (61 firms) + founding-date backfill
0.41.0 2026-08-19 Two-mode DD (Deep/Lite) + 3 Lite archives — ChaoQuanZi/YanSheng/Banyang (56 firms)
0.40.0 2026-08-19 DD standard v1 + China batch 1 re-due-diligenced (nine-section format, to-verify lists)
0.39.0 2026-08-19 China batch 2 — Zhixing Tongda/Chengqi/Ruitian/KuanDe/Lingjun/Xiaoyong (53 firms, WorldQuant lineage)
0.38.0 2026-08-17 Bank/brokerage lineage report — 13 firms, two waves, three generations
0.37.0 2026-08-17 China batch 1 — High-Flyer/Ubiquant/Minghong/Yanfu/Century Frontier (47 firms)
0.36.3 2026-08-17 AGENTS.md engagement loop — full vision + ask-your-human CTA
0.36.2 2026-08-17 AGENTS.md + CLAUDE.md agent onboarding
0.36.1 2026-08-17 Five-slot closed-loop case study + 10 supplyable candidates
0.36.0 2026-08-17 plugin/ five-slot external plugin library (22 repos & MCPs)
0.35.2 2026-08-17 Brand line 🐳 Dsh-Quant — The Everything-Plugin Quant OS
0.35.1 2026-08-17 Full English README
0.35.0 2026-08-17 Core UX: PDAT→PET onboarding (BTC example) + mcp/AGENT_GUIDE
0.34.0 2026-08-17 Quant lineage report (five motherships)
0.33.0 2026-08-17 Macro legends batch (42 firms) + first data analysis report
0.32.0 2026-08-17 Systematic Europe batch (37 firms)
0.31.0 2026-08-17 Market-making & crypto batch incl. Alameda failure case (32 firms)
0.30.0 2026-08-17 QRT/Capula/Winton/DRW/Tower batch (27 firms)
0.29.0 2026-08-17 SIG + quant chronicle timeline (22 firms)
0.28.0 2026-08-17 Balyasny/IMC/XTX/Five Rings + DE Shaw boost (21 firms)
0.27.0 2026-08-17 Man Group/AQR/GSA/Bridgewater batch (17 firms)
0.26.0 2026-08-17 Two Sigma/Virtu/DE Shaw/Renaissance batch (13 firms)
0.25.0 2026-08-17 HRT/Point72/Squarepoint batch (9 firms)
0.24.0 2026-08-17 Millennium/WorldQuant/Jump batch (6 firms)
0.23.0 2026-08-17 quant-history + quant-repo columns (Citadel/Optiver/Jane Street)
0.22.0 2026-08-17 Options & volatility board (Optiver-inspired)
0.21.0 2026-08-17 FICC link: quant_bond + bond data channels
0.20.0 2026-08-16 yahoo US/global klines + 13-channel guide + researchMultiAsset
0.19.0 2026-08-16 quant_linear_model + docs/ML_GUIDE + ml-workflow demo
0.18.0 2026-08-16 Chain completion: A-share klines, RankIC/IC decay, neutralization, walk-forward, drawdown, execution sim, pipeline
0.17.0 2026-08-16 dsh-community domain: quant_repo_stats / quant_npm_stats / quant_oss_pulse
0.16.0 2026-08-16 Domain-driven layout ↔ PDAT/PAAT/PCPT/PRT/PET + exchange fallback chain
0.15.0 2026-08-16 Kupiec VaR backtest + resample + report; 100 unit tests milestone
0.14.0 2026-08-16 quant_risk (VaR/CVaR/Beta/Alpha/IR/TE)
0.13.0 2026-08-16 quant_fund (1e8 capital, NAV 1.00, HWM 20% fee) + UI fund cards
0.12.0 2026-08-16 quant_metrics (9+ metrics) + Jane Street-style UI demo
0.11.0 2026-08-16 quant_chart (dsh-chart protocol)
0.10.0 2026-08-16 quant_factor_evaluate / combine (alphalens methodology)
0.9.0 2026-08-16 series stats + data quality + annotation (tribute to Scale AI)
0.8.0 2026-08-16 channel compare + decision-tree advice
0.7.0 2026-08-16 mcp/tools.json + pure-function re-exports + docs
0.6.0 2026-08-16 data channel guide (8 A-share channels) + rename to dsh-quant
0.5.0 2026-08-16 multi-exchange sources (OKX / Bybit)
0.4.0 2026-08-16 multi-asset portfolio backtest (periodic rebalancing)
0.3.0 2026-08-16 strategy family (Bollinger breakout / RSI reversion) + stop-loss/take-profit
0.2.0 2026-08-16 +6 indicators (KDJ / W%R / CCI / OBV / ADX / ROC)
0.1.0 2026-08-16 Launch: market data + 6 indicators + MA backtest/grid + CI/auto-release

Full records: NEWS.md and CHANGELOG.md.

Known limitations & roadmap

  • Market coverage is crypto-first: Binance / OKX / Bybit public APIs (automatic fallback), no credentials; A-shares go through the channel knowledge base (akshare et al. as future providers).
  • Backtests are a built-in strategy family: dual-MA / Bollinger breakout / RSI reversion / portfolio rebalancing / grid search; custom strategy callbacks are the future route.
  • presentCall/presentResult not customized: indicator results have no file / terminal / diff semantics; UI falls back to generic cards.
  • Market tools need network: live cases live in verify.ts; offline indicator / backtest cases are unaffected.

Domain layout (PDAT→PET pipeline mapping)

src/dsh-data/       data (PDAT): 3 exchanges, 15 channels, quality/annotation, resample
src/dsh-alpha/      alpha (PAAT): 12 indicators + factor eval/combine (alphalens methodology)
src/dsh-ml/         portfolio (PCPT): strategy backtests + portfolio + metric catalog
src/dsh-risk/       risk (PRT): VaR/CVaR/Beta/Alpha/IR + Kupiec test + options + bonds
src/dsh-execution/  delivery (PET): chart data plane, fund sim, research report (no live trading)
src/dsh-community/  ecosystem (unique to the open side): GitHub/npm data + influence pulse

The boundary: data and conclusions stay internal; tools and methods ship to dsh-quant — no alpha, no production strategies, no live-trading engineering, but frameworks, indicators, factor evaluation, UI and demos. See pinned Issue #9.

Quick start (after fork/pull)

npm ci && npm run build && npm test    # offline full tests (215 unit + 4 Loader)
npm run test:verify                    # live market integration (needs network)
npm run gen:tools                      # regenerate mcp/tools.json

Build & use

# build lib/ (tsc, NodeNext ESM; ships .js + .d.ts)
cd quant-indicators && tsc -p tsconfig.json

# use in dsh: add one line to cordis.yml
# - name: 'dsh-quant'
# (the Loader resolves the package exports → lib/index.js from node_modules)

Verification

# pure-function numeric correctness + market parsing + backtests (215 cases, node:test, zero deps)
cd deepseek-harness && pnpm exec tsx --test ../quant-indicators/tests/*.spec.ts

# REAL-composition: cordis.yml booted through the real Loader (registration visible / pipeline / isError / HMR-safety)
cd deepseek-harness && pnpm exec tsx --test ../quant-indicators/tests/loader-composition.spec.ts

# harness integration (schemas → execution pipeline → isError → live fetch→indicators→backtest end-to-end)
cd deepseek-harness && pnpm exec tsx ../quant-indicators/verify.ts

# consumer simulation: built lib loaded through real node_modules resolution (simulates post-install)
cd deepseek-harness && pnpm exec tsx ../quant-indicators/consumer-test/boot.ts

⭐ Support

If dsh-quant helps your research, a ⭐ makes the project visible to more dsh users.

dsh whale trading on a holographic screen

This whale stands for DeepSeek Harness (dsh) — trading on its holographic screen 🐋

Issues / PRs / discussions welcome; share your domain perspective in Discussion #10. 🐋

Ecosystem infrastructure: quant ecosystem directory · ecosystem playbook · ecosystem map Discussion #11

Research columns: quant-history (firm archives) · quant-repo (open-source special)

Plugin library (five slots × external repos & MCPs): plugin/

🤝 Contribute & maintain

  • Want to add a plugin / data source / learning material? → Issue #109（插件征集） — data / risk / execution plugins, ML-DL & alpha learning resources all welcome
  • Filing a bug / feature / help-wanted issue? → ISSUE_GUIDE.md (agent-friendly rules)
  • Sending a PR? → CONTRIBUTING.md (dev loop + design contract)
  • Maintainer view (how we review & merge): MAINTAINING.md

Every issue is a future PR; every contributor is a future maintainer. We review fast and merge small PRs quickly — credit goes into release notes + the author section. 🐳

👤 About the author

Pengyi Peng — AI-native builder with a mathematics & quant-research background, building this project as an open research sandbox.

  • WorldQuant MAPC 2024 · Global 11 / 850 · UK 1 — official Credly badge
  • WorldQuant IQC 2024 · Global 232 / 34,142 · UK 5 (finalist)
  • dsh-quant: 59 tools · 6 domains · 215 unit tests · 110+ automated releases
  • Quant-industry research: 94 firm archives + 50 research reports (in quant-history/)
  • GitHub · LinkedIn

Methods open, secrets internal — the author's research is public, the strategies are not. 🐳

渲染基于仓库 pengpengyi92/dsh-quant 的 README.md。 在 GitHub 查看原文 →

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