TradingView AI Analyst Development
Budget: €250 – €750 EUR
Trading AI MVP – Functional Development Brief
1. Objective
The goal is to develop a Trading AI MVP that analyzes TradingView screenshots uploaded by the user, evaluates potential Scalp, Day, and Swing trade setups, and continuously learns from results and user feedback.
The system should behave like a disciplined, data-driven trader — analyzing, acting, reviewing, and improving setup quality over time.
2. Core Functionality
2.1 Upload & Data Extraction
The user uploads one or several TradingView screenshots (different timeframes and optional heatmap views).
The system extracts key chart information from each image such as:
Current price levels
RSI values and divergences
EMA positions and trends (50, 200)
Support and resistance zones
Volume clusters or POC regions
Visible chart patterns (W, M, range, triangle, etc.)
If the system cannot confidently extract all values, the user may manually enter missing data through a short form.
2.2 AI Market Analysis
Based on the extracted chart data and a predefined trading rulebook, the system generates structured trade ideas.
Each analysis includes:
Trade bias (long / short / no trade)
Entry, Stop Loss, and Take Profit levels
Required conditions for the setup to remain valid
Invalidation rules
Confidence score and reasoning
An alert plan that can be used in TradingView for automated monitoring
Analyses are created separately for Scalp, Day, and Swing time horizons.
3. Trade Lifecycle & Event-Based Reviews
3.1 Setup Activation
Each new trade idea is stored as an active setup and monitored by the system until one of the following outcomes occurs.
3.2 Event Triggers
The review process is event-driven, meaning it starts automatically as soon as the trade outcome becomes clear:
Event Description Action
Take Profit hit Price reaches or exceeds target level Mark setup as successful and start review immediately
Stop Loss hit Price falls below or above stop level Mark setup as failed and start review immediately
Invalidation Setup condition broken (e.g. EMA trend reversal, failed neckline) Mark as invalidated and start review
Timeout fallback No outcome within 24 hours Mark as “not triggered” and archive automatically
This hybrid model ensures that reviews occur either in real time when something happens or after 24 hours if nothing did.
4. Learning & Continuous Improvement
4.1 Feedback Analysis
Once a trade closes (successful, failed, or invalidated), the system performs a post-trade review comparing:
The initial setup parameters
Actual market movement after the setup
The trader’s journal feedback (if provided)
From this, it determines:
Why the trade worked or failed
Which rule or condition was responsible
How the setup confidence or rule weighting should be adjusted
A short “lesson learned” summary
4.2 Rulebook Evolution
All trading rules (e.g., EMA50 reclaim, RSI divergence confirmation, POC bounce) are stored in a central rulebook.
Each rule has a reliability score that automatically updates after each review:
Correct trade → rule reliability increases
Failed trade → rule reliability decreases
Avoided trade with later price drop → reliability increases
This ensures the system learns statistically which trading conditions perform best over time.
4.3 AI Critic Feedback
A secondary AI module (“Critic”) reviews every trade outcome.
It compares the analysis, result, and rulebook, providing short structured feedback such as:
“Entry too early – wait for confirmed EMA50 reclaim.”
“Valid RSI divergence, but missing volume confirmation.”
These insights are logged and used to refine the playbook and analysis behavior.
5. Journal & Mindset Integration
Each analysis connects to a trading journal entry where the user can record:
Emotional and mental state (focus, discipline, confidence, etc.)
Confirmation of position sizing, stop-loss placement, and trade plan
Post-trade reflections on discipline and decision quality
The system correlates mindset data with setup performance and visualizes:
Discipline vs. success rate
Common emotional triggers (FOMO, revenge trades, impatience)
Improvement over time in both performance and mindset stability
6. Dashboard & Reporting
The dashboard provides a clear overview of trading performance and learning progress:
Win rate and average risk/reward per strategy and time horizon
Confidence calibration and setup quality trend
Most common setup failure reasons
Rule improvement leaderboard
Mindset-performance correlation
“What improved this week” — a summary of insights from recent reviews
7. Learning Feedback Loop Summary
User Uploads Charts → System extracts data
AI Analysis → Generates trade ideas & alerts
Monitoring → Watches price evolution in real time
Event Occurs → TP, SL, or Invalidation triggers automatic review
Post-Trade Review → AI explains cause and lesson
Rulebook Update → Adjust reliability scores and future weighting
Dashboard Update → Visualize learning and performance
Mindset Reflection → Personal growth loop continues
Over time, this feedback loop allows the system to recognize higher-probability setups, avoid unnecessary trades, and support a more disciplined trading process.
8. Deliverables for the MVP
Functional system that can:
Accept uploads and extract chart data
Generate structured trade ideas for Scalp, Day, and Swing setups
Monitor market data and detect trade outcomes
Perform automatic post-trade reviews
Store all analyses, outcomes, and feedback in a structured way
User dashboard showing:
Trade performance statistics
Setup success and failure patterns
Learning progress over time
Mindset-related insights
Self-learning rulebook that automatically adjusts reliability scores and confidence weighting after each review.
9. Success Criteria
Reliable and explainable trade analyses (no “black box” logic)
Accurate event detection for TP, SL, and invalidations
Automated and structured post-trade reviews
Visible learning improvement in the dashboard
Continuous alignment with disciplined, data-driven trading principles
In summary:
This MVP should act like a professional trading mentor — observing charts, applying consistent logic, recording outcomes, and learning from every decision.
The system’s purpose is not only to find trades, but to improve the trader’s decision quality, mindset, and long-term profitability through structured learning and feedback.
1. Objective
The goal is to develop a Trading AI MVP that analyzes TradingView screenshots uploaded by the user, evaluates potential Scalp, Day, and Swing trade setups, and continuously learns from results and user feedback.
The system should behave like a disciplined, data-driven trader — analyzing, acting, reviewing, and improving setup quality over time.
2. Core Functionality
2.1 Upload & Data Extraction
The user uploads one or several TradingView screenshots (different timeframes and optional heatmap views).
The system extracts key chart information from each image such as:
Current price levels
RSI values and divergences
EMA positions and trends (50, 200)
Support and resistance zones
Volume clusters or POC regions
Visible chart patterns (W, M, range, triangle, etc.)
If the system cannot confidently extract all values, the user may manually enter missing data through a short form.
2.2 AI Market Analysis
Based on the extracted chart data and a predefined trading rulebook, the system generates structured trade ideas.
Each analysis includes:
Trade bias (long / short / no trade)
Entry, Stop Loss, and Take Profit levels
Required conditions for the setup to remain valid
Invalidation rules
Confidence score and reasoning
An alert plan that can be used in TradingView for automated monitoring
Analyses are created separately for Scalp, Day, and Swing time horizons.
3. Trade Lifecycle & Event-Based Reviews
3.1 Setup Activation
Each new trade idea is stored as an active setup and monitored by the system until one of the following outcomes occurs.
3.2 Event Triggers
The review process is event-driven, meaning it starts automatically as soon as the trade outcome becomes clear:
Event Description Action
Take Profit hit Price reaches or exceeds target level Mark setup as successful and start review immediately
Stop Loss hit Price falls below or above stop level Mark setup as failed and start review immediately
Invalidation Setup condition broken (e.g. EMA trend reversal, failed neckline) Mark as invalidated and start review
Timeout fallback No outcome within 24 hours Mark as “not triggered” and archive automatically
This hybrid model ensures that reviews occur either in real time when something happens or after 24 hours if nothing did.
4. Learning & Continuous Improvement
4.1 Feedback Analysis
Once a trade closes (successful, failed, or invalidated), the system performs a post-trade review comparing:
The initial setup parameters
Actual market movement after the setup
The trader’s journal feedback (if provided)
From this, it determines:
Why the trade worked or failed
Which rule or condition was responsible
How the setup confidence or rule weighting should be adjusted
A short “lesson learned” summary
4.2 Rulebook Evolution
All trading rules (e.g., EMA50 reclaim, RSI divergence confirmation, POC bounce) are stored in a central rulebook.
Each rule has a reliability score that automatically updates after each review:
Correct trade → rule reliability increases
Failed trade → rule reliability decreases
Avoided trade with later price drop → reliability increases
This ensures the system learns statistically which trading conditions perform best over time.
4.3 AI Critic Feedback
A secondary AI module (“Critic”) reviews every trade outcome.
It compares the analysis, result, and rulebook, providing short structured feedback such as:
“Entry too early – wait for confirmed EMA50 reclaim.”
“Valid RSI divergence, but missing volume confirmation.”
These insights are logged and used to refine the playbook and analysis behavior.
5. Journal & Mindset Integration
Each analysis connects to a trading journal entry where the user can record:
Emotional and mental state (focus, discipline, confidence, etc.)
Confirmation of position sizing, stop-loss placement, and trade plan
Post-trade reflections on discipline and decision quality
The system correlates mindset data with setup performance and visualizes:
Discipline vs. success rate
Common emotional triggers (FOMO, revenge trades, impatience)
Improvement over time in both performance and mindset stability
6. Dashboard & Reporting
The dashboard provides a clear overview of trading performance and learning progress:
Win rate and average risk/reward per strategy and time horizon
Confidence calibration and setup quality trend
Most common setup failure reasons
Rule improvement leaderboard
Mindset-performance correlation
“What improved this week” — a summary of insights from recent reviews
7. Learning Feedback Loop Summary
User Uploads Charts → System extracts data
AI Analysis → Generates trade ideas & alerts
Monitoring → Watches price evolution in real time
Event Occurs → TP, SL, or Invalidation triggers automatic review
Post-Trade Review → AI explains cause and lesson
Rulebook Update → Adjust reliability scores and future weighting
Dashboard Update → Visualize learning and performance
Mindset Reflection → Personal growth loop continues
Over time, this feedback loop allows the system to recognize higher-probability setups, avoid unnecessary trades, and support a more disciplined trading process.
8. Deliverables for the MVP
Functional system that can:
Accept uploads and extract chart data
Generate structured trade ideas for Scalp, Day, and Swing setups
Monitor market data and detect trade outcomes
Perform automatic post-trade reviews
Store all analyses, outcomes, and feedback in a structured way
User dashboard showing:
Trade performance statistics
Setup success and failure patterns
Learning progress over time
Mindset-related insights
Self-learning rulebook that automatically adjusts reliability scores and confidence weighting after each review.
9. Success Criteria
Reliable and explainable trade analyses (no “black box” logic)
Accurate event detection for TP, SL, and invalidations
Automated and structured post-trade reviews
Visible learning improvement in the dashboard
Continuous alignment with disciplined, data-driven trading principles
In summary:
This MVP should act like a professional trading mentor — observing charts, applying consistent logic, recording outcomes, and learning from every decision.
The system’s purpose is not only to find trades, but to improve the trader’s decision quality, mindset, and long-term profitability through structured learning and feedback.