# 🎯 Order Flow Graph - Project Proposal

**Created:** 2026-05-15
**Status:** 🆕 Awaiting Your Confirmation
**Agent:** javisver1_bot (Team Lead, Zeus)

---

## 💡 The Core Idea

You asked: *"Can we look at order book, bid vs ask at each price, combine with Graph RAG, and build a graph of the market where we can see buyers and sellers at price levels? Can we use this to spot abnormalities, find when/where price could breakout, or help us jump in/jump out for profit?"*

**Answer:** YES! This is called **Order Flow Analysis** using graph structures.

---

## 🎨 What It Looks Like

### Traditional View (Boring)
```
Price    | Bid Size | Ask Size
---------|----------|----------
2450.00  | 100      | 50
2450.50  | 200      | 150
2451.00  | 150      | 300
```

### Order Flow Graph View (Powerful!)
```
[Price: 2450.00]
  ├── Bids: 100 contracts
  ├── Asks: 50 contracts
  ├── Imbalance: +0.33 (more buyers)
  └── Signal: Neutral

[Price: 2450.50]
  ├── Bids: 200 contracts
  ├── Asks: 150 contracts
  ├── Imbalance: +0.14
  └── 🚨 ANOMALY: ABSORPTION!
       └── Prediction: Upside breakout likely
       └── Action: Jump in long, target 2451.50
```

---

## 🔍 What We Can Detect

### 1. **Absorption** (My Favorite!)

**Pattern:** Large orders absorbing market orders without price moving

**What it means:** "Smart money" is defending a level, absorbing all attacks

**Trading signal:** FADE THE MOVE - expect reversal at absorption level

**Example:**
```
Price hits 2450.00 repeatedly
5000 contracts trade there
Price DOESN'T break through
= Large buyer absorbing all selling pressure
= Jump in long when price bounces off
```

### 2. **Squeeze**

**Pattern:** Narrow order book + high buying/selling pressure

**What it means:** Low liquidity, someone about to make a move

**Trading signal:** WAIT for breakout, then jump in direction

**Example:**
```
Spread only 0.1 point (normally 0.5)
Thin book (only 100 contracts total)
Delta surging (heavy buying)
= Squeeze! Explosion coming
= Jump in after breakout, ride the move
```

### 3. **Exhaustion**

**Pattern:** Order depletion (no more buyers/sellers at key level)

**What it means:** Last push before reversal

**Trading signal:** ENTER at exhaustion, exit 1-2 levels ahead

**Example:**
```
Price hits resistance at 2455.00
Buyers keep trying, get rejected
Order count drops from 100 → 50 → 10
= Buyers exhausted!
= Jump in short, target next support
```

### 4. **Jump-In/Jump-Out Points**

**Jump IN when:**
- Absorption detected (fade the move)
- Exhaustion at key level (reversal play)
- Squeeze breakout (momentum play)

**Jump OUT when:**
- Next level has large orders (take profit)
- Squeeze with unclear direction (avoid whipsaw)
- Target reached (bank profits)

---

## 📊 How It Works

### Step 1: Build the Graph

**Nodes (Price Levels):**
```
PriceLevel 2450.00
  - total_bid: 100
  - total_ask: 50
  - imbalance: +0.33
  - timestamp: 2026-05-15 10:00:00
```

**Edges (Flow):**
```
2450.00 --[NEXT_LEVEL]--> 2450.50
2450.50 --[HAS_ANOMALY]--> Absorption
2450.50 --[PREDICTS]--> Upside breakout
```

### Step 2: Detect Anomalies

**Absorption Algorithm:**
```python
IF volume_at_level > 2x_average_volume
AND price_fails_to_break_through
AND delta_shows_rejection
THEN flag_absorption()
```

**Squeeze Algorithm:**
```python
IF spread < 0.5x_average_spread
AND total_liquidity < 0.3x_average
AND abs(delta) > 2x_average
THEN flag_squeeze()
```

**Exhaustion Algorithm:**
```python
IF order_count_decreasing_rapidly
AND volume_drying_up
AND imbalance_shifting
THEN flag_exhaustion()
```

### Step 3: Generate Signals

**Signal Format:**
```json
{
  "type": "ABSORPTION",
  "severity": 0.85,
  "direction": "bullish",
  "entry_price": 2450.50,
  "target_price": 2452.00,
  "stop_price": 2449.50,
  "confidence": 0.78,
  "reason": "Large ask orders absorbing flow, price failing to break down"
}
```

---

## 🔄 Integration with Graph RAG

**Combined Power:**
```
Order Flow Graph (this project)
    +
Price Relationship Graph (GRAPH_RAG_TRADING_ASSETS)
    =
SUPER SIGNALS
```

**Example:**
- **Order flow:** "Absorption at ask, bullish signal" (75% confidence)
- **Lead-lag:** "XAUTUSDT leads XAUUSD bearish" (60% confidence)
- **Combined:** Signals conflict → STAY OUT

**Another example:**
- **Order flow:** "Absorption at bid, bearish signal" (80% confidence)
- **Lead-lag:** "XAUTUSDT leads XAUUSD bearish" (70% confidence)
- **Combined:** Both agree → HIGH CONFIDENCE SHORT

---

## 📁 Project Structure

```
ORDER_FLOW_GRAPH/
├── ORDER_FLOW_GRAPH.md          # Full project docs
├── ORDER_FLOW_GRAPH_FEEDBACK.md # Open issues
├── README.md                     # Quick start
├── scripts/                      # Python scripts
│   ├── build_order_flow_graph.py
│   ├── detect_anomalies.py
│   ├── generate_signals.py
│   └── backtest_signals.py
├── data/
│   └── order_flow_graph.json
└── outputs/
    ├── signals/
    └── backtests/
```

---

## ⏱️ Implementation Plan

### Phase 1: Prototype (4-6 hours)
1. Load Sierra tick data
2. Compute order flow metrics (delta, imbalance)
3. Build price level nodes
4. Detect simple anomalies
5. Query for patterns

**Deliverable:** Working order flow graph with basic anomaly detection

### Phase 2: Enhanced Detection (6-8 hours)
1. Advanced absorption detection
2. Squeeze detection
3. Exhaustion detection
4. Multi-timeframe (M1/M5/M15)

**Deliverable:** Production-ready anomaly detection

### Phase 3: Graph RAG Integration (4-6 hours)
1. Link order flow to price patterns
2. Combine with lead-lag signals
3. Build composite signals

**Deliverable:** Super signals (order flow + price relationships)

### Phase 4: Backtesting (6-8 hours)
1. Backtest absorption → breakout
2. Calculate edge scores
3. OOS validation

**Deliverable:** Proven edge, ready for trading

---

## 💰 Expected Results

**Hit Rates:**
- Absorption signals: > 65% win rate
- Squeeze signals: > 60% win rate
- Exhaustion signals: > 55% win rate

**Risk-Reward:**
- Jump-in: 1:2 to 1:3 (2-3 pt target, 1 pt stop)
- Jump-out: Protect profits, exit at next level

**False Positives:**
- Target: < 30% false signals

---

## 🤔 Questions for You

Before I start building, I need your decisions:

### 1. Data Source (Critical!)
**Option A:** Sierra tick data (available now)
- ✅ Immediate start
- ❌ No full order book depth
- ❌ Proxy for absorption (infer from price/volume)

**Option B:** Binance order book API (accurate)
- ✅ Full order book depth
- ✅ Real absorption detection
- ❌ Need to collect historical data
- ❌ API rate limits

**My recommendation:** Start with Sierra (Option A) for prototype, add Binance (Option B) in Phase 3 for accuracy

### 2. Graph Complexity
**Option A:** Simplified (aggregate metrics per level)
- ✅ Faster to build
- ✅ Easier to query
- ❌ Less detailed

**Option B:** Detailed (individual orders, participants)
- ✅ More granular
- ✅ Better anomaly detection
- ❌ More complex
- ❌ Needs better data

**My recommendation:** Start simple (Option A), add detail (Option B) if data allows

### 3. Focus
**Option A:** Jump-in/jump-out optimization (your original request)
**Option B:** Breakout prediction (earlier signals)
**Option C:** Both (full implementation)

**My recommendation:** Both (Option C) - they're complementary

---

## 🚀 Ready to Start?

I've created the project structure at:
```
/home/ubuntu/.hermes/workspace/projects/ORDER_FLOW_GRAPH/
```

**To proceed, please confirm:**
1. Data source choice (Sierra vs Binance)
2. Graph complexity (simple vs detailed)
3. Phase 1 scope (prototype vs full)

**Or just say:**
- *"Go with your recommendations"* (I'll start Phase 1 with Sierra data, simple graph)
- *"Focus on jump-in/jump-out only"* (I'll optimize for entry/exit)
- *"Build everything"* (I'll do full Phase 1-4)

---

## 📚 What I've Created

1. **Full project docs:** `ORDER_FLOW_GRAPH.md` (13,000 words, comprehensive)
2. **Feedback system:** `ORDER_FLOW_GRAPH_FEEDBACK.md` (5 open issues logged)
3. **Quick start:** `README.md` (executive summary)
4. **Project structure:** Scripts, data, outputs folders ready
5. **Hub integration:** Added to `PROJECTS.md`

---

**Your move!** Tell me how you want to proceed and I'll start building. 🚀