digiquant

A quant research deskin a glass boxyou own

The research stack an institutional desk would build — research runs daily and portfolio sizes the risk, through backtest to a tearsheet. Open-source and self-hosted, so work that once needed a team runs for one.

$git clone https://github.com/digithings-ai/digithings.git

Open dashboardScroll to explore

marketsconnecting to the live feed…

// by the numbers

The desk, in four numbers.

No projections — every figure is a property of the shipped stack: the subsystems, the pipeline, and the published tearsheets. Live stays zero because nothing is sent to a venue yet.

subsystems
pipeline stages
backtested trades
live orders
// the pipeline

Research in, a tested strategy out.

digiquant is not a hub of services routing messages around; it’s a linear research workflow. You start in a chat, and each stage hands its output to the next until a strategy is ready to run.

  1. 01

    Research

    Ask in plain language. An LLM research loop pulls free macro and market data and proposes directions to test.

    chat · LLM
  2. 02

    Indicators

    Compose validated indicators — moving averages, RSI, ADF, DPSD — from the shared, unit-tested library.

    indicators lib
  3. 03

    Strategy

    Wire indicators into a rules-based strategy with explicit entries, exits, sizing, and risk.

    strategy spec
  4. 04

    Signals

    Generate entry and exit signals across historical bars — deterministic and reproducible.

    signal gen
  5. 05

    Optimize

    Search the parameter space with Optuna against your own objective. Results are in-sample — the tearsheets say so too.

    Optuna
  6. 06

    Backtest

    Replay on a NautilusTrader core with Pine-faithful indicators — RSI on Wilder's smoothing, Bollinger on population sigma — a full trade ledger, and a tearsheet.

    NautilusTrader
  7. 07

    Export

    Export the strategy to TradingView as Pine v5. Live trading is not on yet — paper and live venues wait on a human gate.

    execution · planned
digiquant · research → portfolio
The research desk in a box.
01
Research
02
Portfolio
03
Execution
01 — Research

Reads the market into ranked, sourced theses.

Ten phases turn alt-data, institutional flow and macro into evidence-linked theses — every claim traceable to its source.

02 — Portfolio

Debates the thesis, sizes the conviction.

Thesis review to committed run — multi-agent deliberation, PM direction and risk sizing, with the dissent on record.

03 — Execution

The stage after the book is committed.

Paper routing is ready. Connecting a live venue is your own integration, not a flag we flip.

Research
00
Preflight
config + data-layer check
01
Triage
what changed since last run
02
Alt-data
sentiment, flows, on-chain
03
Institutional
positioning & 13F flow
04
Macro
rates, liquidity, regime
05
Asset class
cross-asset context
06
Equities
sector & single-name
07
Consolidate
merge the evidence
08
Synthesis
ranked theses
09
Publish
to the thesis store
Portfolio
h1
Thesis review
inherit & re-score
h2
Market thesis
exploration
h3
Vehicle map
thesis → instruments
h4
Screener
opportunity filter
h5
Asset analyst
per-name workup
h6
Deliberation
multi-agent debate
h7
PM direction
allocate & gate
h7e
Risk sizing
½-Kelly, ceilings
h9
Commit run
persist & evolve
Execution
In development

Research and portfolio run today. Live execution is next.

Open dashboard
// live book

The research book, valued live.

The paper portfolio the daily pipeline maintains, marked against the same price feed. Positions and weights are the published book; prices tick live during market hours, and P&L is stated as of the last price date.

Loading the book…
// pre-built strategy library

Research-grade systems, ready to explore.

Browse calibrated backtests from the digiquant library — equity, drawdown, trade logs, and full tearsheets for every release. More assets join the catalog as they clear the pipeline.

Full strategy library
L/S
CAGR
Max drawdown
Profit factor
Win rate
Avg trade return
Trades / yr

L/S
CAGR
Max drawdown
Profit factor
Win rate
Avg trade return
Trades / yr

L/S
CAGR
Max drawdown
Profit factor
Win rate
Avg trade return
Trades / yr

SDCABacktest onlySignals +3d delayed
Total return
Max drawdown
Vs buy & hold
Allocated

  1. BTC L/S
  2. ETH L/S
  3. SOL L/S
  4. BTC-SDCA

// pricing

Own it, or have it run for you.

digiquant is open core, and it is built on the same digithings modules you can deploy yourself. Self-host the whole stack at no cost, join the waitlist for managed hosting, or talk to us about enterprise — the same engine either way.

Self-hosted

Free · MIT

Run the full stack on your own infrastructure with your own model keys.

  • All core services
  • Bring your own key
  • Community support

Managed

Coming soon

Hosted digiquant with managed upgrades and observability. In development.

  • Everything in Self-hosted
  • Managed upgrades
  • Hosted tracing (digismith)
Join the waitlist

Enterprise

Contact

Custom deployment, SLAs, and support for regulated environments.

  • Everything in Managed
  • SLA + priority support
  • Deployment assistance
Contact us

Questions

What do I need to self-host?

A container runtime (or a Python environment) and access to an LLM — any LiteLLM-supported provider or a local model. The rest ships in the open-core stack.

How is NautilusTrader licensed?

digiquant builds on NautilusTrader (open source) for all backtest, optimize, and live paths — see the NautilusTrader repository for its current license terms.

Do I bring my own model keys?

Yes. Self-hosting uses your own provider keys (any LiteLLM-supported provider) or a local model; keys stay on your infrastructure.

Are there usage limits?

No artificial request caps on the self-hosted stack. Throughput is bounded only by your own infrastructure and provider limits.