Live on Binance

AI agents that trade — backed by a 13-algorithm model garden.

DeepAlpha invokes autonomous trading agents on any schedule — every 15 minutes, a cron expression, a specific UTC time, or the instant price breaks a level. Agents reason with a production ML stack and execute against your broker.

Request access See how it works →

1.6K

Agent runs

Algorithms in garden

Asset classes covered

200+

Engineered features

01 · Triggers

Four ways to wake an agent.

Most platforms ship with one trigger type. DeepAlpha gives agents four — the same invocation surface covers systematic rebalancing, scheduled research sweeps, one-shot event studies, and tape-speed breakout hunting.

Trigger spec

BTC/USDT ↑ cross 68,500

React the instant a level breaks — sub-second median dispatch from the tape.

Next firings

ARMED · now

agent.pypython · deepalpha/v2

1# wake on price
2from deepalpha import agent, market
3
4@agent.on_trigger(
5    market("BTC/USDT").cross_above(68_500)
6)
7def breakout(m):
8    return m.confirm_breakout()

02 · Model garden

Best per asset, in production.

Thirteen algorithms across four families compete on every symbol. The single best out-of-sample model wins production.

XGBoost · LightGBM · CatBoost

Gradient-boosted trees

Robust nonlinear pattern detection on engineered tabular features.

RandomForest · LogReg · KNN-DTW

Classical ML

Bagging and distance-based baselines that anchor consensus.

TS Forest · ROCKET

Time-series

Specialized estimators for sequential price structure.

DNN · CNN · RNN · LSTM · Transformer

Deep learning

Sequence and attention models for momentum and order flow.

The single best out-of-sample model per asset wins production. Re-validated on a rolling cadence.

Best per asset

The single best out-of-sample model per asset wins production. Re-validated on a rolling cadence.

Per-asset Sharpe ratios, attribution, and validation transcripts are disclosed privately to qualified investors.Request the methodology deck →

Algorithms

Horizons

Asset classes

03 · Alpha Agents

Agents that never sleep.

Spawn autonomous agents on intervals, cron triggers, or macro events. Each agent routes to the model garden's per-asset best pick for entry, exit, and risk decisions — then acts on real-time price action.

3 Active Agents

Best per asset · 4H

Healthy

Day Trader

Every 1h · 24/7·8m ago

Volatility Scanner

Every 2h·22m ago

Breakout Hunter

Every 3h·1h ago

Macro Playbook

Sun 6:00 PM·2d ago

Live feed1D · 4D

BTC/USD

Best pick: BUY

97,150

+0.000%

ETH/USD

Best pick: BUY

3,420

+0.000%

SOL/USD

Best pick: BUY

192.4

+0.000%

Selected models: BTC → XGBoost · ETH → LightGBM · SOL → ExtraTreesBest per-asset model · spawning trade

04 · How it works

From idea to filled order in four steps.

Each trigger invokes an agent that reasons with the per-asset best ML model, attaches the right skills, and routes orders — all with an audit trail and a kill-switch at the broker.

Describe the thesis

A natural-language brief or a Python handler.

NL or code

Choose a trigger

Interval, cron, timestamp, or price action.

4 modes

Attach skills

Market data, model garden, broker connectors.

40+ skills

Agent trades

Orders route to your broker with audit trail and kill-switch.

Live execution

05 · Strategy & Coverage

Full-stack market read.

A unified view of technicals, macro, and flow — translated into tradeable decisions at every bar.

Asset classes

1D – 30D

Prediction horizons

24/7

Market coverage

Algorithms in garden

Signal Inputs

Ichimoku Cloud
Bollinger Bands
RSI divergence
MACD
FOMC impact analysis
Volatility squeeze
Momentum breakout
Cross-asset correlation

06 · Track Record

Track record, on request.

Performance metrics, attribution, and drawdown analysis are disclosed to qualified investors upon request.

Request the deck

Equity curve — illustrative2024 — 2026

Disclosed privately to qualified investors. Contact investor relations for the full deck.