data
Multi-venue feeds. Freshness-anchored. Same client across markets.
from inalpha_data import get_barsWhy Inalpha · the black box vs the ledger
Most AI trading is a black box.Inalpha writes everything down.
the black box
When it loses
"Buy BTC, 0.62 confidence" — and no way to tell which data or which step went wrong.
When it wins
Was that skill, or luck? If you can't reproduce it, you can't trust it.
Who can place a trade
The model holds the keys to your account. One bad prompt becomes a real order.
Inalpha · ledger
01
When it loses
Replay the exact decision: the data it saw, the logic it ran, the reason it gave.
02
When it wins
Every idea and test is on record, so real edge is something you can prove — not a lucky streak.
03
Who can place a trade
The AI only proposes. A rule you set places the order — it can't pull the trigger itself.
An AI that explains itself — and never trades behind your back.
Intelligence · factors that work now
Agents that pick the factors that work now.
factor.timing ranks the factor zoo by rolling Rank IC and surfaces the currently-effective signals — every factor formalized, IC-tested, multiple-testing-checked, and logged with author, timestamp, and the economic-story gate decision.
factor.timing · rolling Rank IC
factor.timing — ranked by 60d rolling Rank IClive · illustrative
momentum_60deffective
residual_reversaleffective
vol_carryeffective
amihud_illiq
pead_drift
value_bm
Research · opposing minds, one synthesis
deep_dive ~ NVDA · live debateillustrative
▲ bulls 00 bears ▼
Research is a debate, not a single voice.
A deep dive convenes technical, fundamental, sentiment, and valuation analysts — and, when you ask, a panel of investing legends. They argue opposing cases, then Inalpha synthesizes the disagreement into one decision on record.
Optional · a panel of investing legends
BuffettLynchWoodBurryDruckenmillerMarks
Evolution · written, sandboxed, mutated
Agents write the strategy — then rewrite it to beat itself.
An agent writes a full strategy in Python. Three sandbox gates clear it before a single backtest runs. Then it mutates under a multi-objective fitness, so no one metric can be gamed — only strategies that beat the baseline survive.
Agent writes
class Strategy(Bar): ...Three sandbox gates
1AST audit
no os / eval / jailbreak imports
2Isolated subprocess
runs walled off from the kernel
3Strategy contract
must subclass + implement on_bar
Multi-objective fitness
sharpe + 0.3·calmar − 0.1·turnover − 1.0·(maxDD > 30%)mutate · evaluate · keep what beats the baseline
Unified kernel · same code, three modes
One codebase.
Three modes.
Strategy code is written once. Backtest, paper, and the live runner all share it — swap only the Clock and the Gateway. Business logic never changes; divergence can only come from physical reality — slippage, latency, data precision.
strategy = MomentumStrategy(params)
clock =SimClock(bars_2024)
gateway =SimGateway()
engine.run(strategy, clock, gateway)
only clock + gateway change · your strategy never does
paper
In-memory matching, backtest engine, persistent paper trading. State is replay-able.
from inalpha_paper import run_backtestresearch
Multi-analyst LLM debate. Opposing stances. No stale numbers passed as insight.
from inalpha_research import debateTrust boundary · the moat
The AI proposes.
It never places the order.
Every order intent walks a one-way path: the agent drafts a plan, a rule you set (or you) approves it, and only then does a single-use, expiring token unlock execution. The model has no tool that reaches the order book directly — not after a jailbreak, not after a hallucination.
LLM agent
trade.create_planAgent drafts a plan
trade.approve_planA rule, or you, approves
trade.execute_planOne-shot token executes
Order book
LLM agent
denied — no direct path for the LLM
Order bookCoverage · twelve markets, one kernel
Same kernel. Same prompts. Same agents.
All markets route through one orchestrator. Add a venue, every agent gets it for free.
orchestrator
research
factor
risk
execution
add a venue — every agent gets it for free
── Where we are honest with you
- 01D-11 shipped: cross-currency cash + an autonomous paper runner — promoted strategies trade a simulated account on live bars, machine-approved through plan/exec, with full decision replay
- 02RiskEngine enforced at the paper HTTP boundary: notional cap / price deviation / drawdown veto / real exchange calendars across every market
- 03No real-capital deployment — the runner trades a paper account, orders matched locally; every step passes plan/exec approval and lands on disk
FAQSEO.md §FAQ
What you're probably wondering.
Honest answers to the questions we get asked most.