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Tradoshi VS mlsql

Compare Tradoshi VS mlsql and see what are their differences

Tradoshi

Tradoshi is the AI trading journal that analyzes your trades, manages your risk and builds your discipline: automatic sync, prop-firm stats, voice check-in. Free plan, no card required.

Rating
0 reviews
Pricing
Freemium Free trial $19.99 / Monthly (Essential)
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Alternatives Is it good?
mlsql

Infer SQL queries from plain-text questions and table headers.

Rating
0 reviews
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Alternatives Is it good?
Usejarvis
Turn business tasks into reusable workflows with company context, AI judgement and human approvals. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, mlsql seems to be more popular. It has been mentioned 2 times since March 2021.

social mentions
0 vs 2
Trading Journal popularity
100% vs 0%
alternatives listed
75 vs 1

Base details

Website, pricing, platforms and company facts side by side.

Tradoshi
mlsql
Website tradoshi.com github.com
Pricing
Freemium Free trial $19.99 / Monthly (Essential) Official pricing
—
Platforms
Web SaaS
—
Company Startup from the United Kingdom · 1 - 9 employees · 2026 —
Listed in
Trading Journal SaaS / Fintech / Payments Stock Trading Trading Finance AI Fintech Stock Market Crypto Forex Investing Analytics +9
Education LMS Online Learning Web App +1

About Tradoshi and mlsql

In their own words, as submitted to SaaSHub.

Tradoshi
mlsql

Tradoshi is an AI-powered trading journal built around what most tools ignore: the trader's psychology. It syncs automatically with 2,024 MetaTrader brokers, plus 98 more reachable through their own API (cTrader, TradeLocker, Interactive Brokers, NinjaTrader, IG and crypto exchanges). It computes...

Read more about Tradoshi

No description of mlsql yet.

Features and specs

What each product offers, as listed by its team.

Tradoshi 11 features
mlsql 0 features
  • Automatic broker sync
    Connects to 2,000+ MT4/MT5 brokers, plus cTrader, DXtrade, TradeLocker, NinjaTrader and IBKR
  • Oshi Score (discipline)
    Six-axis rating of your trading discipline and behavior
  • Voice emotional check-in
    Pre-session voice check that flags the days you shouldn't trade
  • Risk & money management
    Real risk %, R-multiple and drawdown computed from your stops
  • Prop-firm support
    Prop-firm rules, drawdown limits and multi-account tracking
  • Trading journal & stats
    Expectancy, profit factor, win rate, drawdown, R-multiple
  • AI trading coach (Oshi)
    AI coach that analyzes your trades and links emotions to your P&L
  • Backtesting
    Bar-by-bar replay backtesting with realistic execution costs
  • Trade Review
    Replay each trade candle-by-candle to analyze your execution
  • Live session monitoring
    Real-time discipline guardrails with in-app, email & Telegram alerts
  • Mentoring (Mentor Mode)
    Mentor space: student tracking, per-trade comments, chat & video

No features have been listed yet.

Analysis

An editorial look at what each product does well and who it suits.

Tradoshi
mlsql

No analysis of Tradoshi yet.

Overall verdict

  • MLSQL is a solid open-source unified platform that blends SQL with machine learning, making data engineering and ML workflows accessible through a single, declarative language. It's a good choice for teams looking to streamline big data and ML pipelines without switching between multiple tools.

Why this product is good

  • It unifies data processing and machine learning under a single SQL-like syntax, lowering the learning curve for data teams.
  • Built on top of Apache Spark, it leverages a proven distributed computing engine for handling large-scale data.
  • Open-source and actively developed, allowing customization and community-driven improvements.
  • Supports the full ML lifecycle including data preprocessing, training, and deployment within one workflow.
  • Reduces the need to context-switch between separate ETL, analytics, and ML tools.

Recommended for

  • Data engineers and data scientists who prefer SQL-centric workflows
  • Teams working with big data on Apache Spark
  • Organizations wanting to unify ETL and machine learning pipelines
  • Companies seeking an open-source alternative to fragmented ML tooling
  • Analysts looking to build ML models without deep programming expertise

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Tradoshi
mlsql
100% 100%
Trading Journal
0% 0%
0% 0%
Online Learning
100% 100%
100% 100%
Trading
0% 0%
0% 0%
Web App
100% 100%

Questions & Answers

As answered by people managing Tradoshi and mlsql.

What makes your product unique?

Tradoshi's answer

Most trading journals stop at your P&L. Tradoshi goes to the real reason traders lose: their own behavior. It scores your discipline across six axes (the Oshi Score), runs a voice emotional check-in before each session to flag the days you shouldn't trade, and links your emotional state directly to your results, so you see in numbers which mindsets cost you money. It's a journal that coaches your psychology, not just your statistics.

How would you describe the primary audience of your product?

Tradoshi's answer

Active retail and prop-firm / funded traders who take it seriously: forex, indices, crypto and CFD traders on MT4/MT5, IBKR or cTrader, who struggle less with strategy than with discipline, risk and emotional control. Also trading educators and mentors who want to follow their students' real performance and behavior.

Which are the primary technologies used for building your product?

Tradoshi's answer

Tradoshi is a web application (with native iOS and Android planned) that connects in real time to broker platforms (MetaTrader 4/5, Interactive Brokers, cTrader). Its analysis and coaching layer is powered by large language models, and the voice emotional check-in uses voice-emotion AI. Backtesting and trade replay run on institutional-grade tick data.

What's the story behind your product?

Tradoshi's answer

Tradoshi comes from a trader who was ruined by his own emotions, not by a bad strategy. Revenge trading after a loss, oversizing out of greed, breaking his own rules under pressure: he blew up his account from the inside, the way most traders do. Rebuilding it, he realized that what saved him wasn't a new indicator but discipline, strict risk rules and honest daily routines he forced himself to follow. Tradoshi turns those exact tools and routines into software: a SaaS that measures your discipline, checks your emotional state before each session, and helps you become and stay profitable by protecting you from yourself. It exists so other traders don't have to learn this lesson the hard way.

Why should a person choose your product over its competitors?

Tradoshi's answer

Because it fixes what other journals ignore. Classic journals give you great statistics; Tradoshi gives you the statistics plus a six-axis discipline score, a voice emotional check-in, prop-firm rule tracking, live-session guardrails with real-time alerts, backtesting, trade replay and free mentoring, all in one place. It connects automatically to 2,000+ MT4/MT5 brokers (plus cTrader, DXtrade, TradeLocker, NinjaTrader and IBKR), and its AI coach tells you exactly what to correct. Built for the trader who already knows the numbers aren't the whole problem.

User comments

Share your experience with using Tradoshi and mlsql. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Tradoshi 0 mentions
mlsql 2 mentions

Tracking Tradoshi since Jul 2026.

  • Download the files from Docker Container and locally edit them
    I am working on a project that requires me to take user input (as English) and return SQL queries. We will eventually be working with multiple datasets, which is why the valuenet over at https://github.com/paulfitz/mlsql ended up being... Source: over 4 years ago
  • Clone and edit Docker Projects hosted on Github
    I found this great resource here: https://github.com/paulfitz/mlsql. Source: almost 5 years ago

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