Iceberg pro plus
Budget: $1,500 – $3,000 USD
Project: Smart Iceberg Aggregation Smart Discovery System (without display interface)
Goal
Building an analytical AI system (without a display interface)
Analyzes the flow of orders (Order Flow) in the futures market,
Over time, he learns how to automatically adjust his criteria to determine
When Iceberg orders are of real institutional significance (Smart Accumulation)
When is it noise or illusion?
---
Basic idea
Instead of handling each separate Iceberg or holding weights manually,
A system consisting of 3 main layers is built:
1. Buffer Smart Assembly Layer (Smart Time Window)
2. Feature Fusion Vitchers Analysis Layer (CVD, Absorption, Spoof)
AI Optimizer Self Learning Layer (Adaptive Learning Engine)
---
Phase 1: Smart Buffer (Smart Time and Price Compilation)
Idea:
The system does not use a fixed window (such as 60 seconds always),
It learns optimal time from context:
Example: If the market is fast and the volume is high, reduce the Window to 20-30 seconds.
If the market is slow and fluctuating little → expands it to 90 seconds.
How to learn:
Uses intraday oscillation analysis (Volatility Index) + number of events inside the window
Artificial Intelligence Model (AI Model) monitors the relationship between previous forecast results and classification accuracy
Time Window dynamically adjusts with Feedback episode
---
Phase 2: Feature Fusion (Fitchers merged)
Basic characteristics:
For each Cluster the following values are extracted:
"""Vitcher Purpose"" - The Expected Range"
Iceberg Volume Institutional Density 0 → 1
CVD Buy/Sell Pressure -1 → +1
Absorption Institutional Defense Force 0 → 1
Spoof Intensity Fake Command Ratio 0 → 1
They are then combined via the evaluation equation:
Score = (W1 × Iceberg) + (W2 × CVD) + (W3 × Absorption) - (W4 × Spoof)
---
Phase 3: AI Adaptive Optimizer
Idea:
Instead of manually determining weights (W1-W4),
The system uses an online learning model such as:
Reinforcement Learning (RL)
Bayesian Optimization
To learn which weights give the highest accuracy in the previous Clusters ranking.
Method of work:
1. After each window (Cluster) the system reviews the accuracy of the previous classification:
Was the prediction correct (e.g., the trend really continued or reversed? )
2. The model is rewarded with weights that gave higher accuracy.
3. These new weights are used in the next window.
4. Thus the system develops over time without manual intervention.
---
Stage 4: Output Results (Logs only)
The system releases results in Logs or JSON format, without a graphical interface.
Example:
{
"timestamp": "2025-10-08T14:45:00Z",
"price_level": 4562.50,
"iceberg_volume": 620,
"cvd": 340,
"absorption": "Strong",
"spoof": "Low",
"ai_window": 42.5,
"weights": {"w1":0.35",w2":0.25",w3":0.30",w4":0.10},
"classification": "TRUE_BUY_SIDE_ACCUMULATION",
"score": 0.84
}
Each line contains:
Time and Price
Actual values of Vitchers
The Time Window I chose
New Weights
Final result and classification
---
Phase 5: Conflict Resolution (Smart Conflict Resolution)
When Icebergs are buying and selling in the same area:
1. AI calculates the total ratio (Buy vs Sell Ratio).
2. If Buy ≥ 65% → proves the buying trend.
3. If less → Cluster retains as a mixed and reanalyzes it in the next cycle (Recheck Cycle).
---
Technical requirements of the programmer
Element Details
Language Python 3.10+
Basic libraries pandas, numpy, scikit-learn, torch (or tensorflow), json
Local CSV or Memory Buffer database
Learning Type Reinforcement / Online Learning
Outputs Logs or JSON only
Interaction without a display interface, just background Script
---
Final project outputs
1. Full Python code contains:
Buffer Smart Unit
Feature Fusion Unit
AI Optimizer Unit
Log Output Unit
2. Config.json file to save initial settings (Initial Parameters)
3. PDF file Theoretical explanation of the system (without images)
Contains methodology + self-learning logic + performance evaluation method
Goal
Building an analytical AI system (without a display interface)
Analyzes the flow of orders (Order Flow) in the futures market,
Over time, he learns how to automatically adjust his criteria to determine
When Iceberg orders are of real institutional significance (Smart Accumulation)
When is it noise or illusion?
---
Basic idea
Instead of handling each separate Iceberg or holding weights manually,
A system consisting of 3 main layers is built:
1. Buffer Smart Assembly Layer (Smart Time Window)
2. Feature Fusion Vitchers Analysis Layer (CVD, Absorption, Spoof)
AI Optimizer Self Learning Layer (Adaptive Learning Engine)
---
Phase 1: Smart Buffer (Smart Time and Price Compilation)
Idea:
The system does not use a fixed window (such as 60 seconds always),
It learns optimal time from context:
Example: If the market is fast and the volume is high, reduce the Window to 20-30 seconds.
If the market is slow and fluctuating little → expands it to 90 seconds.
How to learn:
Uses intraday oscillation analysis (Volatility Index) + number of events inside the window
Artificial Intelligence Model (AI Model) monitors the relationship between previous forecast results and classification accuracy
Time Window dynamically adjusts with Feedback episode
---
Phase 2: Feature Fusion (Fitchers merged)
Basic characteristics:
For each Cluster the following values are extracted:
"""Vitcher Purpose"" - The Expected Range"
Iceberg Volume Institutional Density 0 → 1
CVD Buy/Sell Pressure -1 → +1
Absorption Institutional Defense Force 0 → 1
Spoof Intensity Fake Command Ratio 0 → 1
They are then combined via the evaluation equation:
Score = (W1 × Iceberg) + (W2 × CVD) + (W3 × Absorption) - (W4 × Spoof)
---
Phase 3: AI Adaptive Optimizer
Idea:
Instead of manually determining weights (W1-W4),
The system uses an online learning model such as:
Reinforcement Learning (RL)
Bayesian Optimization
To learn which weights give the highest accuracy in the previous Clusters ranking.
Method of work:
1. After each window (Cluster) the system reviews the accuracy of the previous classification:
Was the prediction correct (e.g., the trend really continued or reversed? )
2. The model is rewarded with weights that gave higher accuracy.
3. These new weights are used in the next window.
4. Thus the system develops over time without manual intervention.
---
Stage 4: Output Results (Logs only)
The system releases results in Logs or JSON format, without a graphical interface.
Example:
{
"timestamp": "2025-10-08T14:45:00Z",
"price_level": 4562.50,
"iceberg_volume": 620,
"cvd": 340,
"absorption": "Strong",
"spoof": "Low",
"ai_window": 42.5,
"weights": {"w1":0.35",w2":0.25",w3":0.30",w4":0.10},
"classification": "TRUE_BUY_SIDE_ACCUMULATION",
"score": 0.84
}
Each line contains:
Time and Price
Actual values of Vitchers
The Time Window I chose
New Weights
Final result and classification
---
Phase 5: Conflict Resolution (Smart Conflict Resolution)
When Icebergs are buying and selling in the same area:
1. AI calculates the total ratio (Buy vs Sell Ratio).
2. If Buy ≥ 65% → proves the buying trend.
3. If less → Cluster retains as a mixed and reanalyzes it in the next cycle (Recheck Cycle).
---
Technical requirements of the programmer
Element Details
Language Python 3.10+
Basic libraries pandas, numpy, scikit-learn, torch (or tensorflow), json
Local CSV or Memory Buffer database
Learning Type Reinforcement / Online Learning
Outputs Logs or JSON only
Interaction without a display interface, just background Script
---
Final project outputs
1. Full Python code contains:
Buffer Smart Unit
Feature Fusion Unit
AI Optimizer Unit
Log Output Unit
2. Config.json file to save initial settings (Initial Parameters)
3. PDF file Theoretical explanation of the system (without images)
Contains methodology + self-learning logic + performance evaluation method