Founding team brief

A trading operating system, not a journal

Axiom Trader OS is designed to detect mistakes better than the trader, quantify execution quality, classify institutional setups automatically, and turn market decisions into measurable behavioral feedback. It merges broker ingestion, pro charting, SMC detection, AI coaching, and SaaS-grade growth loops.

Explore architecture AI intelligence engine

Equity

$148,260

+$12,840 this month

Sharpe

2.14

Top decile systematic discipline

Profit Factor

1.83

Stable edge across major sessions

Max Drawdown

3.8%

Contained beneath monthly guardrail

Trading Performance Score

84 / 100

Execution improved +9 pts

Institutional dashboard

Equity, drawdown, expectancy and discipline trend in one view.

Live design system

Dynamic win rate

62% London / 68% NY Open

Best edge

FVG continuation after liquidity sweep

Risk control

0.74% avg per idea

Core UVP

Why this can become the category leader.

Defensible moat

Behavioral mistake detection engine

Links sequence of losses, session timing, stop drift, and emotional annotations to identify FOMO, revenge, hesitation, and discipline slippage.

Automated institutional setup classification

Order blocks, FVGs, liquidity pools, inducements, and market structure shifts are detected and attached to every trade contextually.

Proprietary trading performance score

Blends expectancy, risk compliance, process adherence, edge quality, and behavioral consistency into one score that forecasts improvement velocity.

OS-style execution layer

Goals, real-time alerts, strategy templates, replay, reports, and team workflows support both retail traders and prop firms.

Competitive benchmark

Axiom outperforms journals by acting as an operating system.

Benchmark vs market

Platform	Positioning	AI	Institutional setup detection	Behavioral analytics	Moat score
Tradelio	Journal + analytics	Basic summaries	No institutional setup engine	Limited	58
Edgewonk	Manual journal + psychology	Minimal	No automated market structure detection	Good manual workflows	63
Tradervue	Journal + sharing	Minimal	No	Basic tagging	55
Axiom Trader OS	Trading operating system	Institutional mentor + prediction engine	Automated SMC and liquidity classification	Decision-to-result behavioral graph	94

Tradelio

Journal + analytics

Basic summaries

SMC detection

No institutional setup engine

Behavioral analytics

Limited

Edgewonk

Manual journal + psychology

Minimal

SMC detection

No automated market structure detection

Behavioral analytics

Good manual workflows

Tradervue

Journal + sharing

Minimal

SMC detection

Behavioral analytics

Basic tagging

Axiom Trader OS

Trading operating system

Institutional mentor + prediction engine

SMC detection

Automated SMC and liquidity classification

Behavioral analytics

Decision-to-result behavioral graph

Institutional dashboard modules

The app surface should feel like a performance cockpit.

Module stack

Institutional Dashboard

Equity, balance, drawdown, heatmaps, dynamic KPIs, smart alerts, goal tracking.

Automated Trading Journal

Broker ingestion, screenshots, AI tags, replay, PDF reports, search layer.

Pro Charting System

TradingView-class charting with SMC overlays, replay and multi-broker sync.

AI Intelligence Engine

Mistake detection, coaching, predictive scoring, setup classification.

White-label Academy OS

B2B dashboards, cohort analytics, curriculum feedback loops.

Marketplace Layer

Strategies, indicators, bots and academy templates.

Smart alerts and behavior flags

Overtrading pattern detected

medium

Trade frequency rose 46% after the London close with lower expectancy.

Risk drift on NAS100

high

Average stop widened from 18 to 31 points after two losses.

Strong setup alignment

low

SMC continuation trades during New York open hold a 68% win rate.