Octant AI 🐙

Autonomous Research & Quantitative Analysis Workbench

From a spoken investment thesis to a publication-quality quantitative research PDF in under 60 minutes.

5 Agents
_{Fully autonomous pipeline}

18 Math Models
_{GARCH · B-S · PCA · MVO}

12 Data Sources
_{Academic + market + sentiment}

50,000 MC Paths
_{VaR · ES · ruin probability}

IMRaD PDF
_{Goldman-grade LaTeX report}

What Is Octant AI?
The Problem It Solves
Diagram 1 End-to-End Pipeline
Diagram 2 Three-Layer Stack Architecture
Diagram 3 Mathematical Model Dependency Graph
Diagram 4 Data Acquisition Network
Diagram 5 Volatility Analysis Sub-Pipeline
Diagram 6 Reddit Sentiment Pipeline
Diagram 7 Dual Backtesting Engine
Diagram 8 React Frontend Layout
Diagram 9 Report Generation Pipeline
Diagram 10 Partner Technology Map
Mathematical Model Registry
The PULSE Protocol
Performance Metrics
Report Structure
Partner Technologies
Project Structure
Installation
Quick Start
Demo Thesis Examples
Known Limitations

🐙 What Is Octant AI?

Octant AI is a privacy-first, autonomous quantitative research workbench. A quant researcher inputs a natural-language investment thesis spoken via the Reson8 voice API or typed and the system:

Decomposes it into 4–8 independently testable sub-hypotheses using Gemini 2.5 Pro
Retrieves and analyses academic literature from 6 sources (arXiv, Semantic Scholar, OpenAlex, SSRN, CORE, Modern Finance)
Builds a qualifying equity universe across selectable global exchanges with liquidity screening
Downloads and cleans 20+ years of historical price data with corporate action adjustments
Scrapes social sentiment from Reddit (WallStreetBets + 5 other communities) via Playwright + WSBTrends
Runs dual backtests VectorBT for speed, custom engine for explainability with 18 mathematical models applied per hypothesis
Compiles a publication-quality PDF report typeset in LaTeX with matplotlib figures, BibTeX citations, and full statistical appendices

Every stage streams real-time status updates to the React frontend via a custom WebSocket protocol called PULSE.

The Problem It Solves

A senior quantitative researcher at a hedge fund faces a research cycle that typically unfolds over 3–7 days per hypothesis:

Stage	Time Cost	Pain Point
Literature search	6–8 hours	Manual keyword search across disconnected sources
Universe definition	2–4 hours	Exchange selection, sector filtering, liquidity screens
Data sourcing	3–5 hours	No Bloomberg? Manual CSV downloads
Backtesting	4–6 hours	Survivorship bias, corporate actions, transaction costs
Statistical validation	3–4 hours	Multiple testing corrections, bootstrap p-values
Report writing	6–8 hours	LaTeX formatting, chart generation, citation management

Octant AI compresses this to under 60 minutes.

For retail algorithmic traders the 300,000+ users on QuantConnect, Alpaca, and Interactive Brokers the same process takes weeks or never happens at all. Octant AI democratises institutional-grade quantitative research.

Diagram 1 End-to-End Agent Pipeline

flowchart TD
    subgraph INPUT["🎙️ RESEARCHER INPUT"]
        R8["Reson8 Voice-to-Text\nStreaming transcription API"]
        TXT["Text Input\nExchange selector · Time range"]
    end

    subgraph DUST["⚙️  DUST ORCHESTRATOR"]
        INPUT
    end

    A1H["⬡  AGENT 1 Hypothesis Engine\nGemini 2.5 Pro · JSON mode · 4–8 sub-hypotheses\nhypothesis · null · math_method_category · scope · direction"]

    subgraph PARALLEL["⟳  PARALLEL EXECUTION BAND  asyncio.gather"]
        direction LR
        A2["AGENT 2\nLiterature Agent\narXiv · Semantic Scholar\nOpenAlex · SSRN · CORE\nGemini Flash batching\nChromaDB embed + store"]
        A3["AGENT 3\nUniverse Builder\nyfinance · OpenBB · fal.ai\nWSBTrends · Playwright\nOHLCV · fundamentals\nsentiment signals"]
    end

    A4H["⬡  AGENT 4 Backtesting Agent\nVectorBT  +  Custom Explainable Engine\n18 Math Models · Walk-forward 70/30 · Bonferroni · BH · Bayes Sharpe"]

    A5H["⬡  AGENT 5 Report Architect\nGemini 2.5 Pro narrative · pdflatex 2-pass · matplotlib 300 DPI\nIMRaD · BibTeX · listings appendix · LaTeX escape"]

    subgraph OUTPUT["📄 PDF REPORT DELIVERABLE"]
        O1["IMRaD report\ncitations · charts"]
        O2["Backtest code\nappendix · VBT"]
        O3["Metrics tables\nSharpe · VaR · Greeks"]
    end

    DUST -->|"structured thesis string"| A1H
    A1H -->|"hypotheses manifest"| PARALLEL
    A2 -->|"citations DB"| A4H
    A3 -->|"universe dataframe + price matrix"| A4H
    A4H -->|"results manifest · equity curves · metrics"| A5H
    A5H -->|"compiled PDF path → /api/reports/{filename}"| OUTPUT

    style INPUT fill:#2C2C2A,color:#F8F9FA
    style DUST fill:#1a1a1a,color:#00C07A
    style PARALLEL fill:#0d2040,color:#F8F9FA
    style A1H fill:#3d2f88,color:#F8F9FA
    style A2 fill:#0f3a7a,color:#F8F9FA
    style A3 fill:#0a4a3a,color:#F8F9FA
    style A4H fill:#6b3a03,color:#F8F9FA
    style A5H fill:#6b1f0f,color:#F8F9FA
    style OUTPUT fill:#004d30,color:#F8F9FA

PULSE WebSocket emits real-time events at every stage milestones (hypothesis cards, citation cards, ticker cards, metrics results, and report section excerpts) all stream live to the React frontend.

Diagram 2 Three-Layer Stack Architecture

graph TB
    subgraph FE["🖥️  FRONTEND LAYER"]
        direction LR
        F1["React 18\nTypeScript"]
        F2["Tailwind CSS\nDark theme"]
        F3["Recharts\nLive charts"]
        F4["WebSocket\nPULSE client"]
        F5["Reson8\nVoice UI"]
    end

    subgraph OR["⚙️  ORCHESTRATION LAYER"]
        direction LR
        O1["Dust.tt\nAgent nodes"]
        O2["FastAPI\nAPI gateway"]
        O3["PULSE Protocol\nWS emitter"]
        O4["Uvicorn\nASGI server"]
    end

    subgraph CL["🧮  COMPUTATION LAYER"]
        direction LR
        subgraph LLM["LLM"]
            C1["Gemini 2.5 Pro\nReasoning"]
            C2["Gemini Flash\nThroughput"]
        end
        subgraph BT["Backtesting"]
            C3["VectorBT\nBacktest"]
            C4["Custom BT\nExplainable"]
            C5["NumPy/SciPy\nMath core"]
        end
        subgraph DATA["Data"]
            C6["yfinance\nPrice data"]
            C7["OpenBB SDK\nFundamentals"]
            C8["fal.ai\nChart images"]
            C9["Playwright\nReddit scrape"]
            C10["WSBTrends\nMention count"]
        end
    end

    subgraph DL["📦  DELIVERY LAYER"]
        direction LR
        D1["pdflatex\n2-pass compile"]
        D2["matplotlib\nFigure export"]
        D3["pandas\nData tables"]
        D4["PDF output\nDownload link"]
    end

    FE <-->|"WebSocket PULSE · REST HTTP"| OR
    OR <-->|"task dispatch · results return"| CL
    CL -->|"compiled PDF · figures · BibTeX"| DL

    style FE fill:#2a1a5e,color:#F8F9FA
    style OR fill:#0f2a5e,color:#F8F9FA
    style CL fill:#0a3a2a,color:#F8F9FA
    style DL fill:#5e1a0a,color:#F8F9FA
    style LLM fill:#1a0a3e,color:#F8F9FA
    style BT fill:#0a2a1a,color:#F8F9FA
    style DATA fill:#0a2020,color:#F8F9FA

Diagram 3 Mathematical Model Dependency Graph

graph LR
    subgraph RAW["📊 Raw Inputs"]
        P["OHLCV price series"]
        OPT["Options chain IV"]
        MAC["Macro data FRED"]
        SENT["Reddit sentiment WSBTrends"]
        FF5["FF5 factors Ken French"]
    end

    subgraph TS["🌊 Time-Series Models"]
        ADF["ADF stationarity test"]
        ARIMA["ARIMA p,d,q AIC selection"]
        GARCH["GARCH/GJR-GARCH EGARCH fit"]
        HMM["HMM regime 2-state Baum-Welch"]
        FFT["FFT · Wavelet cycle detection"]
    end

    subgraph CS["📐 Cross-Sectional"]
        OLS["OLS factor reg Newey-West SE"]
        ROLL["Rolling regression alpha decay"]
        PCA["PCA · Marchenko-Pastur filter"]
        BS["Black-Scholes Greeks · IV solve"]
        OU["Ornstein-Uhlenbeck half-life fit"]
    end

    subgraph PORT["💼 Portfolio Models"]
        GBM["GBM · Merton jump-diffusion"]
        MVO["MVO efficient frontier LW shrink"]
        BAY["Bayesian updating posterior Sharpe"]
        HYP["Hypothesis tests t-stat · bootstrap"]
        MC["Monte Carlo 50k path sim"]
    end

    subgraph RISK["⚠️ Risk / Output"]
        SH["Sharpe · Sortino Info · Omega"]
        VAR["VaR · ES 95% · 99%"]
        DD["Max drawdown Calmar ratio"]
        ALPHA["Factor alpha Bonferroni p-val"]
        VOL["Vol surface Greeks · RR25"]
    end

    P --> ADF & ARIMA & GARCH & HMM & FFT
    P --> OLS & PCA & OU
    OPT --> BS
    FF5 --> OLS
    SENT --> FFT
    MAC --> BAY
    SENT --> HYP

    ARIMA --> OLS
    GARCH --> ROLL & BS
    HMM --> OU

    OLS --> BAY
    ROLL --> MVO
    PCA --> MVO
    BS --> GBM
    OU --> MC
    OLS --> HYP
    P --> GBM

    GBM --> VAR
    MVO --> SH
    BAY --> ALPHA
    HYP --> ALPHA
    MC --> VAR & DD
    BS --> VOL

    style RAW fill:#2C2C2A,color:#F8F9FA
    style TS fill:#0f2a5e,color:#F8F9FA
    style CS fill:#0a3a2a,color:#F8F9FA
    style PORT fill:#5e3a03,color:#F8F9FA
    style RISK fill:#5e1a0a,color:#F8F9FA

Key equations inline: GBM: dS = μS dt + σS dW(t) | OU: dX = κ(θ − X)dt + σdW(t) | EGARCH: ln σ²ₜ = ω + α|εₜ₋₁/σₜ₋₁| + γ(εₜ₋₁/σₜ₋₁) + β ln σ²ₜ₋₁

Diagram 4 Data Acquisition Network

graph TB
    subgraph ACAD["📚 Academic / Literature Sources"]
        direction LR
        AX["arXiv q-fin\nREST · no key\nST·PM·TR·MF·RM"]
        SS["Semantic Scholar\n100 req/s free\ntldr · influence scores"]
        OA["OpenAlex\nno auth · 250M works\nconcept tags"]
        SR["SSRN\nPlaywright scraper\nworking papers"]
        CO["CORE API\n10k req/mo free\nfull-text PDFs"]
        MF["Modern Finance\nopen-access OA\nPyMuPDF extract"]
    end

    subgraph MKTD["📈 Market Data Sources"]
        direction LR
        YF["yfinance\nOHLCV · 20yr · global\nauto_adjust=True"]
        OB["OpenBB SDK\nfundamentals · short int\nFRED macro data"]
        FA["fal.ai\nfast inference API\nchart image gen"]
    end

    subgraph SENTI["💬 Sentiment Sources"]
        direction LR
        WB["WSBTrends Go\nsubprocess call\nmention counts JSON"]
        PW["Playwright\nheadless Chromium\nWSB · 6 subreddits"]
        GF["Gemini Flash\nsentiment extraction\nstructured JSON out"]
    end

    HUB["⚡ FastAPI Data Layer\nAsync Hub"]

    subgraph AGENTS["Destination Agents"]
        AG2["Agent 2\nLiterature"]
        AG3["Agent 3\nUniverse"]
        AG4["Agent 4\nBacktester"]
    end

    AX & SS & OA & SR & CO & MF --> HUB
    YF & OB & FA --> HUB
    WB & PW & GF --> HUB

    HUB --> AG2
    HUB --> AG3
    HUB --> AG4

    style ACAD fill:#2a1a5e,color:#F8F9FA
    style MKTD fill:#0a3a2a,color:#F8F9FA
    style SENTI fill:#5e3a03,color:#F8F9FA
    style HUB fill:#0f2a5e,color:#F8F9FA
    style AGENTS fill:#2C2C2A,color:#F8F9FA

Exchange Universe Coverage

Exchange	Suffix	Region	Notes
NYSE	(none)	US	Full S&P 500 universe
NASDAQ	(none)	US	Tech-weighted
LSE	`.L`	UK	FTSE 350 constituents
TSX	`.TO`	Canada	TSX Composite
ASX	`.AX`	Australia	ASX 200
Euronext Paris	`.PA`	France	CAC 40
Euronext Amsterdam	`.AS`	Netherlands	AEX
Frankfurt XETRA	`.DE`	Germany	DAX
Tokyo	`.T`	Japan	Nikkei 225
Hong Kong	`.HK`	HK	Hang Seng

Liquidity screen applied to all exchanges: minimum average daily volume 500k shares · minimum price $1.00 · maximum estimated bid-ask spread 1%

Diagram 5 Volatility Analysis Sub-Pipeline

flowchart TD
    PRI["Price Return Series  rₜ = ln Sₜ/Sₜ₋₁\n+ Options Chain  strikes K · maturities T · market prices Cᵐᵏᵗ"]

    subgraph GFAM["GARCH Family BIC Model Selection"]
        direction LR
        G1["GARCH 1,1\nσ²ₜ = ω + αε²ₜ₋₁ + βσ²ₜ₋₁\nMLE · α+β < 1"]
        G2["GJR-GARCH\n+ γ·𝟙[εₜ₋₁<0]·ε²ₜ₋₁\nLeverage effect γ"]
        G3["EGARCH\nln σ²ₜ = ω + α|εₜ₋₁/σₜ₋₁|\n+ γ(εₜ₋₁/σₜ₋₁) + β ln σ²ₜ₋₁"]
    end

    CVOL["Conditional Volatility Series  σ̂ₜ\nBIC-selected model · α̂+β̂ persistence reported · vol shock half-life"]

    subgraph REGIME["Regime Detection"]
        HMM2["2-State HMM on σ̂ₜ\nBaum-Welch EM · hmmlearn\nP high-vol regime t"]
    end

    subgraph IVSURF["Implied Volatility Surface"]
        IVS["Invert B-S: Cᵐᵏᵗ = Cᴮˢ σᴵᵛ\nBrent root-finding per K,T\nVol smile · RR 25 skew"]
    end

    subgraph MCSIM["Monte Carlo Simulation"]
        MCB["GBM + Merton jump-diffusion\nN = 50,000 paths · Cholesky Σ\nCorrelated asset paths"]
    end

    subgraph GREEKS["Option Greeks"]
        GRK["Δ = N d₁   Γ = N' d₁ / Sσ√T\nΘ = −SN' d₁ σ/2√T − rKe⁻ʳᵀN d₂\nV = S√T·N' d₁   ρ = KTe⁻ʳᵀN d₂"]
    end

    subgraph VOUT["Volatility Outputs"]
        VO1["VaR 95%  VaR 99%  ES\n1-day · 10-day · 1-month\nCalmar · max DD · ruin p"]
        VO2["Vol Surface Heatmap\nTerm structure slope\nVRP: σᴵᵛ − σᴿᵛ spread"]
    end

    PRI --> G1 & G2 & G3
    G1 & G2 & G3 --> CVOL
    CVOL --> REGIME
    CVOL --> MCSIM
    PRI --> IVSURF
    REGIME --> MCSIM
    IVSURF --> GREEKS
    MCSIM --> VO1
    GREEKS --> VO2
    IVSURF --> VO2

    style GFAM fill:#0f2a5e,color:#F8F9FA
    style REGIME fill:#5e3a03,color:#F8F9FA
    style IVSURF fill:#5e1a0a,color:#F8F9FA
    style MCSIM fill:#2a1a5e,color:#F8F9FA
    style GREEKS fill:#2a1a5e,color:#F8F9FA
    style VOUT fill:#004d30,color:#F8F9FA

Volatility Model Specifications

Model	Equation	Key Parameter	Library
GARCH(1,1)	`σ²ₜ = ω + αε²ₜ₋₁ + βσ²ₜ₋₁`	`α+β` persistence	`arch.arch_model`
GJR-GARCH	`+ γ·𝟙[εₜ₋₁<0]·ε²ₜ₋₁`	`γ` leverage effect	`arch.arch_model`
EGARCH	`ln σ²ₜ = ω + α\|z\| + γz + β ln σ²ₜ₋₁`	No positivity constraint	`arch.arch_model`
HMM Regime	2-state Gaussian · Baum-Welch	Transition matrix P	`hmmlearn`
B-S IV Solve	Brent root-find `C_BS(σ) = C_market`	Bracket [1e-6, 10.0]	`scipy.optimize.brentq`

Diagram 6 Reddit Sentiment Pipeline

flowchart LR
    subgraph SRC["Reddit Sources"]
        direction TB
        WSB["r/WallStreetBets\nHot · New feeds"]
        STK["r/stocks  r/investing\nr/ValueInvesting"]
        SEC["Sector subs\nr/EnergyInvestors etc"]
        GO["WSBTrends Go binary\nsubprocess · mention\ncount JSON output"]
    end

    subgraph PLS["Playwright Scraper"]
        direction TB
        CHR["Headless Chromium\nrotate UA · delay N 5,1.5 s\nasync playwright-python"]
        PST["Post titles · upvotes\nTop-50 comments\npost time · ticker regex"]
        RAW["Raw text corpus\nper ticker per window\nupvote-weighted"]
        MRG["Merge streams\nmention share norm\nrelative_mention i,t"]
    end

    subgraph NLP["Gemini Flash NLP"]
        direction TB
        DIR["Directional classification\nbullish / bearish / neutral\ns i,t ∈ −1,+1"]
        OPF["Options flow extraction\ncalls · puts · spreads\noverrides raw sentiment"]
        CNV["Conviction level\nhigh / spec / hedging\ncatalyst tags"]
        SCR["Raw score per ticker\nupvote-weighted avg\ns i,t ∈ −1,+1"]
    end

    subgraph SIG["Signal Construction"]
        direction TB
        EWM["EWMA smoothing\nspan=5 days\nnoise reduction"]
        ZSC["Z-score standardise\n90-day rolling μ,σ\nz_sent i,t"]
        REG["Add to factor model\nRᵢₜ = α + β·FF5 + β·z_sent + ε"]
        INT["Interaction tests\nsentiment × vol regime\nsentiment × momentum"]
    end

    WSB & STK & SEC --> CHR
    GO --> MRG
    CHR --> PST --> RAW --> MRG
    PST --> DIR
    RAW --> OPF & CNV
    MRG --> SCR
    DIR --> OPF --> CNV --> SCR
    SCR --> EWM --> ZSC --> REG --> INT

    style SRC fill:#2C2C2A,color:#F8F9FA
    style PLS fill:#0f2a5e,color:#F8F9FA
    style NLP fill:#2a1a5e,color:#F8F9FA
    style SIG fill:#0a3a2a,color:#F8F9FA

Sentiment Signal Formula

$$z_{sent}(i,t) = \frac{s(i,t) - \mu_{90}(i)}{\sigma_{90}(i)}$$

Where $s(i,t)$ is the upvote-weighted directional score, $\mu_{90}$ and $\sigma_{90}$ are rolling 90-day mean and standard deviation. The signal is then added as regressor: $R_{it} = \alpha + \sum_j \beta_j F_{jt} + \beta_k z_{sent}(i,t) + \varepsilon_{it}$

Playwright Anti-Detection

Measure	Implementation
Request delay	`random.normalvariate(5, 1.5)` seconds, clamped to [3, 8]
User agent	Current Chrome stable UA string, rotated per session
Navigation pattern	Random subreddit order, variable comment depth 20–50
Content wait	`page.wait_for_selector('.Post', timeout=15000)`
Rate limiting	Graceful degradation pipeline continues without sentiment on timeout

Diagram 7 Dual Backtesting Engine

flowchart TD
    TOP["Hypothesis Manifest + Universe Dataframe"]
    SIG["Signal Constructor\nFactor z-scores · OU spreads · GARCH σ̂ₜ · sentiment z_sent · B-S Greeks"]
    WF["Walk-Forward Data Split\nIn-sample 70%  ──→  Out-of-sample 30%\nRe-estimated on each fold · OOS results reported separately"]

    subgraph VBT_ENGINE["VectorBT Engine"]
        direction TB
        V1["Vectorised NumPy arrays\nentry/exit bool matrices\nfractional shares"]
        V2["Transaction cost model\ncommission + spread\n2bp low / 10bp high"]
        V3["Portfolio stats\nall metrics in one pass\nL/S · L-only configs"]
    end

    subgraph CUST_ENGINE["Custom Explainable Engine"]
        direction TB
        C1["Day-by-day Python loop\nexplicit position state\nfull trade log DataFrame"]
        C2["Per-trade record\nentry/exit signal val · GARCH regime\nholding period · r_net"]
        C3["Conditional analysis\nregime-conditional perf\nsignal decay within trade"]
    end

    RES["Results Manifest Combined Engine Output"]

    subgraph METRICS_R["Return Metrics"]
        direction LR
        M1["Sharpe ratio"] 
        M2["Sortino ratio"]
        M3["Info ratio"]
        M4["Omega ratio"]
        M5["CAGR"]
        M6["Total return"]
        M7["Turnover"]
    end

    subgraph METRICS_K["Risk Metrics"]
        direction LR
        M8["Max drawdown"]
        M9["DD duration"]
        M10["Calmar ratio"]
        M11["VaR 95/99"]
        M12["ES CVaR"]
        M13["Ruin prob"]
    end

    subgraph METRICS_S["Statistical"]
        direction LR
        M14["t-statistic"]
        M15["bootstrap p"]
        M16["Bonferroni"]
        M17["BH-FDR"]
        M18["Factor α t"]
        M19["Bayes Sharpe"]
    end

    TOP --> SIG --> WF
    WF --> VBT_ENGINE
    WF --> CUST_ENGINE
    V1 --> V2 --> V3
    C1 --> C2 --> C3
    V3 --> RES
    C3 --> RES
    RES --> METRICS_R & METRICS_K & METRICS_S

    style VBT_ENGINE fill:#0f2a5e,color:#F8F9FA
    style CUST_ENGINE fill:#0a3a2a,color:#F8F9FA
    style METRICS_R fill:#0f2a5e,color:#F8F9FA
    style METRICS_K fill:#5e3a03,color:#F8F9FA
    style METRICS_S fill:#5e1a0a,color:#F8F9FA

Trade Log Fields (Custom Engine)

Every trade in the custom engine produces a record with these fields:

entry_date · exit_date · ticker · entry_price · exit_price · entry_signal_value · exit_signal_value · holding_period_days · return_gross · return_net · transaction_costs_bps · garch_vol_at_entry · regime_state_at_entry · signal_zscore_at_entry

Diagram 8 React Frontend Three-Panel Layout

graph TB
    subgraph APP["⟨App/⟩ React 18 + TypeScript + Tailwind bg #0A0B0D · accent #00C07A"]
        direction LR

        subgraph LP["⟨LeftPanel/⟩  280px fixed"]
            direction TB
            L1["VoiceInput.tsx\nReson8 streaming mic\npulsing green animation"]
            L2["ThesisInput.tsx\nfree-form textarea\nchar count"]
            L3["ExchangeSelector.tsx\nNYSE·NASDAQ·LSE\nTSX·ASX·Euronext\nFrankfurt·Tokyo·HK"]
            L4["TimeRange.tsx\nbacktest period\nstart + end date"]
            L5["SectorFilter.tsx\noptional sector"]
            L6["RunButton.tsx\npulses green · stop\ndisabled while running"]
        end

        subgraph CP["⟨CenterPanel/⟩  flexible width"]
            direction TB
            C1["PipelineView.tsx\n5 agent stage cards\nPULSE events → status · progress · log"]
            C2["HypothesisCards.tsx\n4–8 cards · math badge\nnull hypothesis italic"]
            C3["CitationCards.tsx\nrelevance score pill\ngreen border = supports · red = contradicts"]
            C4["TickerGrid.tsx\nsparkline images fal.ai\nexchange badge · mktcap"]
            C5["ResultsMatrix.tsx\nhypothesis × metric heat table\ngreen/amber/dim cells"]
            C6["ActivityLog.tsx\nlast 10 lines streaming\nmonospace · auto-scroll"]
        end

        subgraph RP["⟨RightPanel/⟩  320px fixed"]
            direction TB
            R1["ReportOutline.tsx\nsection headers live fill\ngreen dot = written\npulsing = writing"]
            R2["SectionExcerpt.tsx\nprose preview streaming\nGemini output live"]
            R3["DownloadPDF.tsx\nglow animation on ready\nContent-Disposition attachment"]
            R4["TopMetrics.tsx\nSharpe · Max DD\nHypotheses tested\nPapers retrieved"]
        end

        WS["━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\nWebSocket PULSE event stream from FastAPI backend · reconnect exp backoff ×5\n━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"]
    end

    WS -.->|"PULSE events"| C1 & C5 & C6 & R1

    style APP fill:#0A0B0D,color:#F8F9FA
    style LP fill:#1a1a1a,color:#F8F9FA
    style CP fill:#0d1a2a,color:#F8F9FA
    style RP fill:#2a0d0d,color:#F8F9FA
    style WS fill:#004d30,color:#00C07A

Voice Input Flow Reson8

Browser mic → MediaRecorder → 250ms audio chunks
     ↓
WebSocket → /api/voice/transcribe → Reson8 streaming API
     ↓
Partial transcript → PULSE payload_type="transcription" → ThesisInput.tsx
     ↓
2-second silence detected → "transcription_complete" → thesis locked → RunButton active

Diagram 9 Report Generation Pipeline

flowchart TD
    INP["Agent 5 Inputs\nHypotheses Manifest · Citations DB · Results Manifest"]

    subgraph GEMNARR["Gemini 2.5 Pro Narrative Writing  streaming output"]
        direction TB
        N1["Abstract  250 words"]
        N2["§1 Introduction · objective · scope"]
        N3["§2 Literature review synthesis\nprior art table · BibTeX"]
        N4["§3 Data · universe construction\nsurvivorship bias note"]
        N5["§4 Methodology\nall 18 models · amsmath notation"]
        N6["§5 Results interpretation\nfactor α discussion"]
        N7["§6 Discussion · caveats\nrobustness · alpha decay"]
        N8["§7 Conclusions · next steps"]
    end

    subgraph FIGGEN["matplotlib Figure Generation  300 DPI PNG"]
        direction TB
        F1["Equity curves + drawdown\nbenchmark overlay · stats box"]
        F2["Vol surface 3D mesh\n+ heatmap flat view"]
        F3["Return distribution\nhistogram + Q-Q plot"]
        F4["Correlation clustermap\nseaborn · diverging cmap"]
        F5["Rolling Sharpe + CI\n12-month window"]
        F6["Eigenvalue spectrum\nMarchenko-Pastur boundary"]
        F7["Sentiment z-score\nwavelet coherence"]
        F8["Factor loading heatmap\nhypothesis × FF5"]
    end

    TEMPL["LaTeX Template Assembly\n\\documentclass[12pt,a4paper]{article}\ngeometry · amsmath · booktabs · hyperref · biblatex · listings · tikz title page"]

    subgraph PROC["Pre-processing"]
        direction LR
        ESC["Escape: & % $ # _ { } ~ ^ \\\nBibTeX entry insert\n\\addbibresource{references.bib}"]
        INJ["\\includegraphics PNG inject\n\\cite key linking\nlistings: VectorBT code\nappendices A–D"]
    end

    COMP["pdflatex Two-Pass Compile  cross-references\nstdout/stderr captured · error log parsed · LatexCompilationError on fail"]

    PDF["📄 PDF DELIVERABLE\nIMRaD · citations · figures · appendices · /api/reports/{filename}"]

    INP --> GEMNARR & FIGGEN
    N1 --> N2 --> N3 --> N4 --> N5 --> N6 --> N7 --> N8
    F1 --> F2 --> F3 --> F4 --> F5 --> F6 --> F7 --> F8
    GEMNARR --> TEMPL
    FIGGEN --> TEMPL
    TEMPL --> ESC & INJ
    ESC & INJ --> COMP --> PDF

    style GEMNARR fill:#2a1a5e,color:#F8F9FA
    style FIGGEN fill:#0a3a2a,color:#F8F9FA
    style TEMPL fill:#5e3a03,color:#F8F9FA
    style PROC fill:#5e3a03,color:#F8F9FA
    style COMP fill:#5e1a0a,color:#F8F9FA
    style PDF fill:#004d30,color:#F8F9FA

LaTeX Packages Used

Package	Purpose
`geometry`	A4 paper, 2cm margins
`amsmath` + `amssymb`	All mathematical notation
`booktabs` + `array`	Professional metrics tables
`graphicx`	`\includegraphics` PNG figures
`biblatex`	BibTeX citation management
`listings`	Syntax-highlighted backtest code
`hyperref`	Clickable cross-references
`microtype`	Professional typographic refinement
`tikz`	Title page header decoration
`lmodern` + `[T1]{fontenc}`	Full font encoding

Diagram 10 Partner Technology Integration Map

graph LR
    subgraph SPINE["Pipeline Spine"]
        direction TB
        AG1["Agent 1\nHypothesis Engine"]
        AG2["Agent 2\nLiterature Agent"]
        AG3["Agent 3\nUniverse Builder"]
        AG4["Agent 4\nBacktesting Agent"]
        AG5["Agent 5\nReport Architect"]
    end

    subgraph GEMINI["🔵 Google DeepMind"]
        GM["Gemini 2.5 Pro\nHypothesis decomposition\nPaper abstract synthesis\nLaTeX narrative writing\nResults interpretation\n─────────────────\nGemini Flash\nParallel abstract batches\nReddit NLP extraction\ntext-embedding-004 ChromaDB"]
    end

    subgraph RESON8["🟢 Reson8"]
        RS["Hyper-Customisable STT\nconsole.reson8.dev · API key\ndocs.reson8.dev · Europe-first\n─────────────────\nStreaming transcription endpoint\n250ms audio chunk streaming\n2s silence → finalise thesis\nHands-free thesis dictation"]
    end

    subgraph FAL["🔴 fal.ai"]
        FL["Fast Inference API\n\$50 credit · free tier\ntecheurope-amsterdam coupon\n─────────────────\nUse 1 Universe Builder\nStyled sparkline PNG per ticker\nfal-client Python SDK\n─────────────────\nUse 2 Report Architect\nVol surface cover art PNG\n\\includegraphics title page"]
    end

    subgraph DUSTT["🟡 Dust.tt"]
        DU["Agent Orchestration Layer\n5 agent nodes defined\nInput/output schema validation\nError handling + retry logic\nExponential backoff with jitter\n─────────────────\nSession state management\nParallel band A2 ∥ A3 trigger\nTimeout: A1=60s A2/A3=300s\nA4=600s A5=300s\nPipelineStoppedError support"]
    end

    GM -->|"decomposes"| AG1
    GM -.->|"synthesises"| AG2
    GM -.->|"writes"| AG5
    RS -->|"voice → thesis"| AG1
    FL -->|"sparklines"| AG3
    FL -.->|"cover art"| AG5
    DU -->|"orchestrates all"| AG1
    DU --> AG2 & AG3 & AG4 & AG5

    subgraph BADGE["Hackathon Compliance"]
        BC["✅ 4 of 6 partner technologies integrated\nMinimum 3 required: Exceeded"]
    end

    style GEMINI fill:#0f2a5e,color:#F8F9FA
    style RESON8 fill:#0a3a2a,color:#F8F9FA
    style FAL fill:#5e1a0a,color:#F8F9FA
    style DUSTT fill:#5e3a03,color:#F8F9FA
    style SPINE fill:#2C2C2A,color:#F8F9FA
    style BADGE fill:#004d30,color:#F8F9FA

Mathematical Model Registry

All 18 models are applied per hypothesis by Agent 4.

1. ADF Stationarity Test

Tests $H_0$: unit root present in the return series. The ADF statistic is compared to critical values at 1%, 5%, and 10% significance. If the price series is non-stationary (almost always), the first-differenced log return series is tested. Integration order d is recorded per ticker and passed to ARIMA.

Implementation: statsmodels.tsa.stattools.adfuller

2. ARIMA(p,d,q) Modelling

Grid search over $p \in {0..5}$, $q \in {0..5}$, $d$ fixed from ADF result. Model selection by:

$$\text{AIC} = 2k - 2\ln(\hat{\mathcal{L}})$$

Fitted coefficients, AIC, and one-step-ahead forecast residuals stored per ticker.

Implementation: statsmodels.tsa.arima.model.ARIMA

3–5. GARCH Family (three variants, BIC-selected)

GARCH(1,1): $$\sigma^2_t = \omega + \alpha\varepsilon^2_{t-1} + \beta\sigma^2_{t-1}, \quad \alpha+\beta < 1$$

GJR-GARCH (leverage effect): $$\sigma^2_t = \omega + \alpha\varepsilon^2_{t-1} + \gamma,\mathbf{1}[\varepsilon_{t-1}<0]\varepsilon^2_{t-1} + \beta\sigma^2_{t-1}$$

EGARCH (no positivity constraint): $$\ln\sigma^2_t = \omega + \alpha\left|\frac{\varepsilon_{t-1}}{\sigma_{t-1}}\right| + \gamma\frac{\varepsilon_{t-1}}{\sigma_{t-1}} + \beta\ln\sigma^2_{t-1}$$

Selection by $\text{BIC} = k\ln(n) - 2\ln(\hat{\mathcal{L}})$.

Implementation: arch.arch_model with dist='Normal'

6. HMM Volatility Regime Detection

2-state Hidden Markov Model fitted to the GARCH conditional variance series $\hat{\sigma}^2_t$. States: low-vol (State 0), high-vol (State 1). Transition probability matrix $\mathbf{P}$ and emission parameters estimated via Baum-Welch EM. Output: $P(\text{high-vol regime} \mid \text{data}, t)$ for each date.

Implementation: hmmlearn.hmm.GaussianHMM

7. FFT + Wavelet Analysis

FFT: numpy.fft.fft on return series → power spectrum $|X(f)|^2$. Fisher's g-statistic identifies peaks significantly above expected flat i.i.d. spectrum.

Wavelet: Morlet continuous wavelet transform via pywt.cwt detects time-varying correlation between sentiment signal and returns. Output: wavelet coherence heatmap PNG.

8. OLS Fama-French 5-Factor Regression

$$R_{it} = \alpha_i + \beta_1\text{MKT}_t + \beta_2\text{SMB}_t + \beta_3\text{HML}_t + \beta_4\text{MOM}_t + \beta_5\text{RMW}_t + \beta_6\text{CMA}t + \varepsilon{it}$$

OLS estimator: $\hat{\beta} = (X'X)^{-1}X'y$. Standard errors: Newey-West HAC with lag $= \lfloor 4(T/100)^{2/9} \rfloor$. Factor data from Ken French's free library.

Implementation: statsmodels.OLS with cov_type='HAC'

9. Rolling Regression Alpha Decay

12-month rolling window OLS. Rolling $\hat{\alpha}_t$ series plotted over time. Sharpe ratio of the $\hat{\alpha}_t$ series measures consistency. Strategies with declining recent alpha flagged as "potential decay."

10. Black-Scholes Model + Greeks

$$C = SN(d_1) - Ke^{-rT}N(d_2), \quad d_1 = \frac{\ln(S/K) + (r+\sigma^2/2)T}{\sigma\sqrt{T}}, \quad d_2 = d_1 - \sigma\sqrt{T}$$

Greek	Formula
Delta	$\Delta = N(d_1)$
Gamma	$\Gamma = N'(d_1) / (S\sigma\sqrt{T})$
Theta	$\Theta = -SN'(d_1)\sigma/(2\sqrt{T}) - rKe^{-rT}N(d_2)$
Vega	$\mathcal{V} = S\sqrt{T}N'(d_1)$
Rho	$\varrho = KTe^{-rT}N(d_2)$

Implied vol: Brent's method scipy.optimize.brentq, bracket [1e-6, 10.0].

11. Ornstein-Uhlenbeck Process

$$dX(t) = \kappa(\theta - X(t)),dt + \sigma,dW(t)$$

Discrete OLS fit: $\Delta X_t = \alpha + \beta X_{t-1} + \varepsilon_t$

$$\kappa = -\ln(1+\beta)/\Delta t, \quad \theta = -\alpha/\beta, \quad \text{half-life} = \ln(2)/\kappa$$

Pairs with half-life < 20 trading days flagged as strong mean-reversion candidates.

12. GBM + Merton Jump-Diffusion

GBM: $dS = \mu S,dt + \sigma S,dW(t)$ → $S(t) = S(0)\exp!\left((\mu - \sigma^2/2)t + \sigma W(t)\right)$

Note: Itô correction $-\sigma^2/2$ is non-trivial (8 pp/yr for $\sigma=40%$).

Merton: $dS/S = (\mu - \lambda\bar{k})dt + \sigma dW + (e^J - 1)dN(t)$

Where $N(t) \sim \text{Poisson}(\lambda)$, $J \sim \mathcal{N}(\mu_J, \sigma_J^2)$, $\bar{k} = e^{\mu_J + \sigma_J^2/2} - 1$.

Jump identification: returns exceeding $3\hat{\sigma}_t$ (GARCH-scaled).

13. Monte Carlo Simulation 50,000 Paths

GBM discretisation: $S(t+\Delta t) = S(t)\exp!\left((\mu - \sigma^2/2)\Delta t + \sigma\sqrt{\Delta t},Z_t\right)$

Correlated portfolio: $\mathbf{R}(t) = \boldsymbol{\mu}\Delta t + L\mathbf{Z}(t)\sqrt{\Delta t}$ where $L$ is the lower Cholesky factor of $\hat{\Sigma}$ (Ledoit-Wolf shrinkage). If $\Sigma$ not PD → Higham (1988) nearest-PD algorithm applied.

Output: VaR and ES at $\alpha \in {95%, 99%}$ for $h \in {1\text{-day}, 10\text{-day}, 1\text{-month}}$.

14. PCA + Marchenko-Pastur Noise Filter

$$\Sigma = VDV', \quad \lambda_{max} = \sigma^2(1 + \sqrt{q})^2, \quad q = N/T$$

Eigenvalues above $\lambda_{max}$ carry genuine signal. Below: consistent with random noise. Retained PCs used for: dimensionality reduction in large universes, factor model robustness, correlation visualisation.

15. Mean-Variance Optimisation + Ledoit-Wolf

$$\min_w; w'\hat{\Sigma}w \quad \text{s.t.}\quad w'\mu = \mu^*, ; \sum_i w_i = 1, ; w_i \geq 0$$

Ledoit-Wolf shrinkage: $\hat{\Sigma} = (1-\delta)\Sigma_{sample} + \delta\Sigma_{target}$, optimal $\delta^*$ computed analytically.

Implementation: scipy.optimize.minimize(method='SLSQP')

16. Hypothesis Testing Battery

t-test: $t = \bar{R} / (\sigma_R / \sqrt{T})$ vs $t_{T-1}$. Two-tailed + one-sided.

Bootstrap Sharpe p-value: $B = 10{,}000$ resamples. $p = \text{fraction}(\widehat{SR}{boot} \geq \widehat{SR}{obs})$.

Bonferroni: threshold $\alpha/N$ for $N$ hypotheses.

Benjamini-Hochberg FDR: reject $H_i$ if $p_{(i)} \leq (i/N)\alpha$. Controls FDR at 5%.

Labels: strongly significant (Bonferroni ✓) · significant (BH only) · not significant (both fail).

17. Bayesian Updating

Prior: $\text{SR} \sim \mathcal{N}(\mu_0, \sigma_0^2)$ from literature effect sizes (extracted by Gemini Flash).

$$\mu_{post} = \frac{\sigma_0^{-2}\mu_0 + (T/\sigma^2)\widehat{SR}}{\sigma_0^{-2} + T/\sigma^2}$$

Precision-weighted average. Reported as "Bayes-adjusted Sharpe" a more conservative and calibrated estimate.

18. Survivorship Bias Correction

yfinance only provides currently-listed tickers. Correction applied to CAGR:

Cap Tier	Annual Correction
Large-cap	−0.5%/yr
Mid-cap	−1.5%/yr
Small-cap	−2.0%/yr

Cited methodology: Harvey, Liu & Zhu (2016) "And the Cross-Section of Expected Returns" also motivates raising the t-stat significance threshold from 2.0 to 3.0 for published factors.

⚡ The PULSE WebSocket Protocol

PULSE (Proprietary Unified Live Status Emission) is the custom WebSocket event schema that streams agent state to the frontend in real time.

{
  "type": "PULSE",
  "agent": "hypothesis_engine | literature | universe | backtest | architect",
  "status": "pending | active | complete | error",
  "progress": {
    "current_step": 3,
    "total_steps": 8,
    "percent_complete": 37,
    "estimated_remaining_sec": 45
  },
  "payload_type": "status | hypothesis_card | citation_card | ticker_card | metric_result | report_section | transcription | error",
  "payload": { },
  "message": {
    "title": "Analysing arXiv papers",
    "subtitle": "Processing batch 3/12..."
  },
  "timestamp": "2025-03-21T10:45:00Z"
}

Extended Payload Types

`payload_type`	Fields
`hypothesis_card`	`id` · `statement` · `null` · `math_badge` · `direction`
`citation_card`	`title` · `authors` · `year` · `journal` · `relevance` · `supports` · `abstract_summary`
`ticker_card`	`symbol` · `name` · `exchange` · `sector` · `sparkline_url` · `mktcap` · `short_interest`
`metric_result`	`hypothesis_id` · `sharpe` · `sortino` · `max_dd` · `cagr` · `alpha_t` · `p_value` · `bonferroni_pass` · `bh_pass`
`report_section`	`section_name` · `excerpt` · `is_complete`
`transcription`	`partial_text` · `is_final`

Performance Metrics All 18

Category	Metric	Formula / Notes
Return	Total return	Cumulative P&L
Return	CAGR	$(1+R_{total})^{252/T} - 1$
Return	Annualised excess return	Above risk-free rate
Return	Benchmark excess return	Above benchmark CAGR
Risk	Annualised volatility	$\sigma_{daily} \times \sqrt{252}$
Risk	Maximum drawdown	$\max_{t \leq \tau} [S_\tau - S_t] / S_t$
Risk	DD duration	Peak-to-recovery in trading days
Risk	Calmar ratio	CAGR / Max drawdown
Risk	Drawdown-at-risk 95%	DD exceeded in 5% of rolling windows
Risk-adj	Sharpe ratio	$(R_p - R_f) / \sigma_p$
Risk-adj	Sortino ratio	$(R_p - R_f) / \sigma_{downside}$
Risk-adj	Information ratio	$(R_p - R_b) / \sigma_{tracking}$
Risk-adj	Omega ratio	$\int_L^\infty [1-F(r)]dr / \int_{-\infty}^L F(r)dr$
Statistical	t-statistic	$\bar{R} / (\sigma_R / \sqrt{T})$
Statistical	Bootstrap p-value	B=10,000 resamples
Statistical	Factor alpha t-stat	From FF5 OLS regression
Statistical	Bonferroni threshold	$\alpha / N$ hypotheses
Statistical	Bayes-adjusted Sharpe	Normal-Normal posterior mean
Cost	Break-even cost (bps)	Net return = 0 threshold
Cost	Return at 2bp/10bp	Sensitivity analysis
Volatility	GARCH persistence	$\hat{\alpha} + \hat{\beta}$
Volatility	Regime fraction high-vol	% of days in HMM State 1
Sentiment	Sentiment factor loading	$\hat{\beta}k$ on $z{sent}$

Report Structure IMRaD Format

The final PDF follows the academic IMRaD format adapted for quantitative finance:

octant_research_YYYY-MM-DD.pdf
│
├── Title Page ............ thesis · researcher · date · top-line metrics · watermark
├── Abstract .............. 250 words · results-first · Goldman note style
│
├── §1 Introduction ....... investment thesis · market context · scope · structure
├── §2 Literature Review .. thematic synthesis · prior art table · BibTeX citations
│   └── Prior Art Table ... N papers supporting · N contradicting · avg effect size
├── §3 Data & Universe .... summary stats · cleaning · liquidity screen · surv. bias
├── §4 Methodology ........ all 18 models · amsmath notation · backtest framework
├── §5 Results ............ per-hypothesis metrics table · equity curves · vol surface
│   ├── Hypothesis × Metric table (Sharpe · MaxDD · Alpha t · Bonferroni · BH)
│   ├── Equity curve charts (per hypothesis)
│   ├── Factor regression tables (FF5 loadings)
│   └── Volatility surface heatmap (when options data available)
├── §6 Discussion ......... alpha decay · robustness · limitations · data snooping
└── §7 Conclusion ......... top-ranked hypothesis · implementation · next steps
│
├── Appendix A ............ Mathematical derivations (B-S PDE · Itô · GARCH likelihood)
├── Appendix B ............ Backtest code (VectorBT + custom engine · listings pkg)
├── Appendix C ............ Raw results (full trade log first 100 · per-ticker metrics)
└── Appendix D ............ Data provenance (DOIs · API versions · access dates)

Figures Generated

Figure	Chart Type	Key Content
Equity curves	Line chart	Cumulative log-return · benchmark overlay · drawdown shaded red
Volatility surface	3D mesh + heatmap	IV over (K,T) grid · smile · term structure
Return distribution	Histogram + Q-Q	Fitted Normal overlay · kurtosis · skewness annotation
Correlation clustermap	Seaborn clustermap	Hierarchical clustering · blue-white-red diverging
Rolling Sharpe	Line + CI band	12-month rolling window · dashed zero line
Eigenvalue spectrum	Bar chart	MP boundary overlay · signal vs noise PCs
Wavelet coherence	Colourmap	Time-varying correlation: sentiment ↔ returns
Factor loading heatmap	Heatmap	Hypothesis × FF5 factor · colour = loading magnitude

Hackathon Partner Technologies

Partner	Integration Points	Why Substantive
Google DeepMind	Agent 1 decomposition · Agent 2 paper analysis · Agent 5 report writing · ChromaDB embeddings	Gemini is the cognitive engine at every reasoning step
Reson8	Voice input → streaming transcription → thesis auto-populate	Demo moment: speak a thesis, system immediately begins research
fal.ai	Ticker sparkline images (Agent 3) · vol surface cover PNG (Agent 5)	Two distinct use-cases; graceful degradation on failure
Dust.tt	All 5 agent nodes · retry logic · parallel A2∥A3 · session state	Replaces custom state-machine; manages timeout/backoff

4 of 6 partner technologies integrated: minimum 3 requirement exceeded

Project Structure

octant-ai/
├── backend/
│   ├── main.py                     # FastAPI app · CORS · WebSocket /ws/{session_id}
│   ├── config.py                   # Pydantic Settings · all env vars
│   ├── pulse.py                    # PulseEmitter class · all emit methods
│   ├── orchestrator.py             # OctantOrchestrator · pipeline coordinator
│   ├── session_manager.py          # SessionState · stop flags · in-memory store
│   ├── exceptions.py               # All custom exceptions with recovery_action
│   ├── logging_config.py           # structlog · JSON prod · pretty dev · session ctx
│   ├── health.py                   # /health · service checks · latency
│   ├── agents/
│   │   ├── hypothesis_engine.py    # Agent 1 · Gemini 2.5 Pro · JSON mode
│   │   ├── literature_agent.py     # Agent 2 · 6 sources · ChromaDB
│   │   ├── universe_builder.py     # Agent 3 · yfinance · OpenBB · fal.ai
│   │   ├── backtesting_agent.py    # Agent 4 · VectorBT + custom engine
│   │   └── report_architect.py     # Agent 5 · Gemini narrative · pdflatex
│   ├── math_engine/
│   │   ├── time_series.py          # ADF · ARIMA · GARCH/GJR/EGARCH · HMM · FFT
│   │   ├── cross_sectional.py      # OLS · rolling regression · PCA · MP filter
│   │   ├── options_models.py       # Black-Scholes · Greeks · IV surface · Brent
│   │   ├── stochastic.py           # GBM · Merton · OU process · Cholesky · Higham
│   │   ├── portfolio.py            # MVO · Ledoit-Wolf · efficient frontier · VaR/ES
│   │   ├── hypothesis_tests.py     # t-test · bootstrap · Bonferroni · BH · Bayes
│   │   └── performance.py          # PerformanceCalculator · all 18 metrics
│   ├── data/
│   │   ├── price_fetcher.py        # yfinance wrapper · liquidity screen · log returns
│   │   ├── fundamentals.py         # OpenBB SDK · short int · mktcap · FRED macro
│   │   ├── ff5_factors.py          # Ken French CSV · daily factors · local cache
│   │   ├── fal_client.py           # fal.ai sparkline + cover art generation
│   │   ├── literature_sources.py   # arXiv · Sem Scholar · OpenAlex · CORE
│   │   ├── scraper_ssrn.py         # Playwright SSRN abstract scraper
│   │   ├── scraper_reddit.py       # Playwright Reddit · 6 subs · ticker regex
│   │   ├── modern_finance_scraper.py # Playwright + PyMuPDF PDF extraction
│   │   ├── wsb_trends.py           # Go binary subprocess · async · JSON parse
│   │   └── chroma_store.py         # ChromaDB · embed · store · similarity query
│   ├── sentiment/
│   │   └── signal_constructor.py   # 5-step pipeline · EWMA · z-score · interaction
│   ├── report/
│   │   ├── latex_template.py       # LaTeXAssembler · full .tex construction
│   │   ├── figure_generator.py     # matplotlib · 8 chart types · 300 DPI PNG
│   │   ├── pdf_compiler.py         # pdflatex 2-pass · error parsing · async
│   │   └── bibtex_builder.py       # @article · @misc · DOI · clean authors
│   ├── voice/
│   │   └── reson8_client.py        # Streaming transcription · silence detection
│   ├── routers/
│   │   ├── pipeline.py             # POST /start · POST /stop · GET /status
│   │   └── voice.py                # WebSocket /api/voice/transcribe
│   ├── tests/
│   │   └── test_math_engine.py     # Unit tests: ADF · GARCH · B-S · OU · Higham
│   └── requirements.txt
│
├── frontend/
│   ├── src/
│   │   ├── App.tsx                 # CSS Grid 3-panel layout · session UUID
│   │   ├── components/
│   │   │   ├── LeftPanel/
│   │   │   │   ├── VoiceInput.tsx  # MediaRecorder · binary chunks · Reson8
│   │   │   │   ├── ThesisInput.tsx
│   │   │   │   ├── ExchangeSelector.tsx
│   │   │   │   ├── TimeRange.tsx
│   │   │   │   ├── SectorFilter.tsx
│   │   │   │   └── RunButton.tsx   # Green glow · disabled state
│   │   │   ├── CenterPanel/
│   │   │   │   ├── PipelineView.tsx
│   │   │   │   ├── AgentCard.tsx   # Expandable · progress bar · status dot
│   │   │   │   ├── HypothesisCards.tsx  # Stagger 200ms · math badge
│   │   │   │   ├── CitationCards.tsx    # Green/red border · relevance pill
│   │   │   │   ├── TickerGrid.tsx       # Sparkline img · exchange badge
│   │   │   │   ├── ResultsMatrix.tsx    # Heat table · green/amber/dim
│   │   │   │   └── ActivityLog.tsx      # Monospace · auto-scroll
│   │   │   └── RightPanel/
│   │   │       ├── ReportOutline.tsx    # Green dot · pulsing = writing
│   │   │       ├── SectionExcerpt.tsx
│   │   │       ├── DownloadPDF.tsx      # Glow on ready
│   │   │       └── TopMetrics.tsx       # 2×2 metric card grid
│   │   ├── hooks/
│   │   │   ├── usePulseWebSocket.ts     # Reconnect · typed router · audio send
│   │   │   └── useVoiceInput.ts         # MediaRecorder lifecycle
│   │   ├── types/
│   │   │   └── pulse.ts                 # Full TypeScript PULSE interfaces
│   │   └── utils/
│   │       └── formatters.ts
│   ├── package.json
│   ├── tailwind.config.js               # OctDeep · OctGreen · OctGreenDk
│   └── tsconfig.json
│
├── reports/                             # Generated PDF output directory
├── data/                                # ChromaDB local store · FF5 cache
├── latex_templates/                     # .tex template files
├── dust_workflow.json                   # Dust agent nodes · schemas · retry
├── .env.example
├── Dockerfile                           # Python 3.11-slim + texlive-full
├── docker-compose.yml                   # backend + frontend services
└── README.md

Installation

Prerequisites

Docker + Docker Compose (recommended)
or Python 3.11+ · Node.js 18+ · texlive-full (for pdflatex)
Go 1.21+ (to compile WSBTrends binary)

API Keys Required

Service	Where to Get	Environment Variable
Google Gemini	aistudio.google.com	`GEMINI_API_KEY`
Reson8	console.reson8.dev	`RESON8_API_KEY`
fal.ai	fal.ai · coupon: `techeurope-amsterdam`	`FAL_API_KEY`
Dust.tt	dust.tt	`DUST_API_KEY`
OpenBB	openbb.co optional free tier	`OPENBB_TOKEN`

With Docker (Recommended)

git clone https://github.com/AtillaErsezen/Octant-AI.git
cd Octant-AI

# Copy and fill in your API keys
cp .env.example .env
nano .env

# Build and run
docker-compose up --build

Frontend → http://localhost:3000 · Backend API → http://localhost:8000 · API docs → http://localhost:8000/docs

Manual Setup

# Backend
cd backend
pip install -r requirements.txt
cp ../.env.example ../.env     # fill in API keys

# Compile WSBTrends Go binary
git clone https://github.com/wbollock/wsbtrends /tmp/wsbtrends
cd /tmp/wsbtrends && go build -o wsbt . && cp wsbt /path/to/octant-ai/data/
# Update WSBT_BINARY_PATH in .env

# Install pdflatex (Ubuntu/Debian)
sudo apt-get install texlive-full

# Run backend
uvicorn backend.main:app --reload --host 0.0.0.0 --port 8000

# Frontend (new terminal)
cd frontend
npm install
npm run dev

Quick Start

Open http://localhost:3000
Click the 🎙️ microphone button and speak your thesis or type it in the text area
Select your target exchanges (NYSE, NASDAQ, LSE, etc.)
Set your backtest time range (default: 10 years)
Optionally set a sector filter
Press Run
Watch the pipeline execute live in the center panel
Download your PDF report from the right panel when complete

Typical completion time: 45–90 minutes for a universe of 50–100 tickers with full literature review and 50k Monte Carlo paths.

Demo Thesis Examples

"Test a mean-reversion strategy on NVDA that enters when RSI(14) < 30 
and Z-Score(Vol) > 2. Benchmark against QQQ."

"I want to test whether low short-interest momentum stocks in the energy 
sector outperform during rising real yield environments."

"Test momentum reversal in small-cap energy stocks when VIX spikes above 30."

"Does implied volatility skew in the technology sector predict subsequent 
equity returns over a 10-day horizon?"

"Test whether the Ornstein-Uhlenbeck mean reversion half-life of 
semiconductor pairs decreases during Fed tightening cycles."

Known Limitations

Limitation	Details	Mitigation Applied
Survivorship bias	yfinance only provides currently-listed tickers	Correction factor applied: 0.5–2.0%/yr by cap tier
Data snooping	Testing N hypotheses inflates false-positive rate	Bonferroni + Benjamini-Hochberg corrections; t-stat threshold 3.0 per Harvey et al.
Options data depth	Historical options chains limited on free tier	Current-date IV surface via Yahoo Finance; OptionsDX for historical
SSRN scraping	No official API; Playwright-scraped	Graceful degradation if blocked; pipeline continues
LaTeX compile time	pdflatex 2-pass on complex documents can take 60–90s	Runs async; PDF appears in right panel when ready
Reddit rate limits	Reddit throttles server-side requests	Playwright humanised delays; graceful degradation
Go binary (WSBTrends)	Requires manual compile	Graceful degradation if binary not found; sentiment layer skipped

License

MIT License see LICENSE

Octant AI 🐙 · Built at {Tech: Europe} Amsterdam AI Hackathon · March 2025

The future of quantitative finance is open.

Name		Name	Last commit message	Last commit date
Latest commit History 30 Commits
assets		assets
backend		backend
frontend		frontend
.env.example		.env.example
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
docker-compose.yml		docker-compose.yml
dust_workflow.json		dust_workflow.json

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Octant AI 🐙

Autonomous Research & Quantitative Analysis Workbench

Table of Contents

🐙 What Is Octant AI?

The Problem It Solves

Diagram 1 End-to-End Agent Pipeline

Diagram 2 Three-Layer Stack Architecture

Diagram 3 Mathematical Model Dependency Graph

Diagram 4 Data Acquisition Network

Exchange Universe Coverage

Diagram 5 Volatility Analysis Sub-Pipeline

Volatility Model Specifications

Diagram 6 Reddit Sentiment Pipeline

Sentiment Signal Formula

Playwright Anti-Detection

Diagram 7 Dual Backtesting Engine

Trade Log Fields (Custom Engine)

Diagram 8 React Frontend Three-Panel Layout

Voice Input Flow Reson8

Diagram 9 Report Generation Pipeline

LaTeX Packages Used

Diagram 10 Partner Technology Integration Map

Mathematical Model Registry

⚡ The PULSE WebSocket Protocol

Extended Payload Types

Performance Metrics All 18

Report Structure IMRaD Format

Figures Generated

Hackathon Partner Technologies

Project Structure

Installation

Prerequisites

API Keys Required

With Docker (Recommended)

Manual Setup

Quick Start

Demo Thesis Examples

Known Limitations

License

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