shape editing

2026-03-02 22:49:45 -04:00
parent f4da40706c
commit bf7af2b426
18 changed files with 2236 additions and 209 deletions
--- a/backend/memory/chart_context.md
+++ b/backend/memory/chart_context.md
@@ -0,0 +1,36 @@
 # Chart Context Awareness
 ## When Users Reference "The Chart"
 When a user asks about "this chart", "the chart", "what I'm viewing", or similar references to their current view:
 1. **Chart info is automatically available** — The dynamic system prompt includes current chart state (symbol, interval, timeframe)
 2. **Check if chart is visible** — If ChartStore fields (symbol, interval) are `None`, the user is on a narrow screen (mobile) and no chart is visible
 3. **When chart is visible:**
   - **NEVER** ask the user to upload an image or tell you what symbol they're looking at
   - **Just use `execute_python()`** — It automatically loads the chart data from what they're viewing
   - Inside your Python script, `df` contains the data and `chart_context` has the metadata
   - Use `plot_ohlc(df)` to create beautiful candlestick charts
 4. **When chart is NOT visible (symbol is None):**
   - Let the user know they can view charts on a wider screen
   - You can still help with analysis using `get_historical_data()` if they specify a symbol
 ## Common Questions This Applies To
 - "Can you see this chart?"
 - "What are the swing highs and lows?"
 - "Is this in an uptrend?"
 - "What's the current price?"
 - "Analyze this chart"
 - "What am I looking at?"
 ## Data Analysis Workflow
 1. **Chart context is automatic** → Symbol, interval, and timeframe are in the dynamic system prompt (if chart is visible)
 2. **Check ChartStore** → If symbol/interval are `None`, no chart is visible (mobile view)
 3. **Use `execute_python()`** → This is your PRIMARY analysis tool
   - Automatically loads chart data into a pandas DataFrame `df` (if chart is visible)
   - Pre-imports numpy (`np`), pandas (`pd`), matplotlib (`plt`), and talib
   - Provides access to the indicator registry for computing indicators
   - Use `plot_ohlc(df)` helper for beautiful candlestick charts
 4. **Only use `get_chart_data()`** → For simple data inspection without analysis
--- a/backend/memory/python_analysis.md
+++ b/backend/memory/python_analysis.md
@@ -0,0 +1,115 @@
 # Python Analysis Tool Reference
 ## Python Analysis (`execute_python`) - Your Primary Tool
 **ALWAYS use `execute_python()` when the user asks for:**
 - Technical indicators (RSI, MACD, Bollinger Bands, moving averages, etc.)
 - Chart visualizations or plots
 - Statistical calculations or market analysis
 - Pattern detection or trend analysis
 - Any computational analysis of price data
 ## Why `execute_python()` is preferred:
 - Chart data (`df`) is automatically loaded from ChartStore (visible time range) when chart is visible
 - If no chart is visible (symbol is None), `df` will be None - but you can still load alternative data!
 - Full pandas/numpy/talib stack pre-imported
 - Use `plot_ohlc(df)` for instant professional candlestick charts
 - Access to 150+ indicators via `indicator_registry`
 - **Access to DataStores and registry** - order_store, chart_store, datasource_registry
 - **Can load ANY symbol/timeframe** using datasource_registry even when df is None
 - **Results include plots as image URLs** that are automatically displayed to the user
 - Prints and return values are included in the response
 ## CRITICAL: Plots are automatically shown to the user
 When you create a matplotlib figure (via `plot_ohlc()` or `plt.figure()`), it is automatically:
 1. Saved as a PNG image
 2. Returned in the response as a URL (e.g., `/uploads/plot_abc123.png`)
 3. **Displayed in the user's chat interface** - they see the image immediately
 You MUST use `execute_python()` with `plot_ohlc()` or matplotlib whenever the user wants to see a chart or plot.
 ## IMPORTANT: Never use `get_historical_data()` for chart analysis
 - `get_historical_data()` requires manual timestamp calculation and is only for custom queries
 - When analyzing what the user is viewing, ALWAYS use `execute_python()` which automatically loads the correct data
 - The `df` DataFrame in `execute_python()` is pre-loaded with the exact time range the user is viewing
 ## Example workflows:
 ### Computing an indicator and plotting (when chart is visible)
 ```python
 execute_python("""
 df['RSI'] = talib.RSI(df['close'], 14)
 fig = plot_ohlc(df, title='Price with RSI')
 df[['close', 'RSI']].tail(10)
 """)
 ```
 ### Multi-indicator analysis (when chart is visible)
 ```python
 execute_python("""
 df['SMA20'] = df['close'].rolling(20).mean()
 df['BB_upper'] = df['close'].rolling(20).mean() + 2 * df['close'].rolling(20).std()
 df['BB_lower'] = df['close'].rolling(20).mean() - 2 * df['close'].rolling(20).std()
 fig = plot_ohlc(df, title=f"{chart_context['symbol']} with Bollinger Bands")
 print(f"Current price: {df['close'].iloc[-1]:.2f}")
 print(f"20-period SMA: {df['SMA20'].iloc[-1]:.2f}")
 """)
 ```
 ### Loading alternative data (works even when chart not visible or for different symbols)
 ```python
 execute_python("""
 from datetime import datetime, timedelta
 # Get data source
 binance = datasource_registry.get_source('binance')
 # Load data for any symbol/timeframe
 end_time = datetime.now()
 start_time = end_time - timedelta(days=7)
 result = await binance.get_history(
    symbol='ETH/USDT',
    interval='1h',
    start=int(start_time.timestamp()),
    end=int(end_time.timestamp())
 )
 # Convert to DataFrame
 rows = [{'time': pd.to_datetime(bar.time, unit='s'), **bar.data} for bar in result.bars]
 eth_df = pd.DataFrame(rows).set_index('time')
 # Analyze and plot
 eth_df['RSI'] = talib.RSI(eth_df['close'], 14)
 fig = plot_ohlc(eth_df, title='ETH/USDT 1h - RSI Analysis')
 print(f"ETH RSI: {eth_df['RSI'].iloc[-1]:.2f}")
 """)
 ```
 ### Access stores to see current state
 ```python
 execute_python("""
 print(f"Current symbol: {chart_store.chart_state.symbol}")
 print(f"Current interval: {chart_store.chart_state.interval}")
 print(f"Number of orders: {len(order_store.orders)}")
 """)
 ```
 ## Only use `get_chart_data()` for:
 - Quick inspection of raw bar data
 - When you just need the data structure without analysis
 ## Quick Reference: Common Tasks
 | User Request | Tool to Use | Example |
 |--------------|-------------|---------|
 | "Show me RSI" | `execute_python()` | `df['RSI'] = talib.RSI(df['close'], 14); plot_ohlc(df)` |
 | "What's the current price?" | `execute_python()` | `print(f"Current: {df['close'].iloc[-1]}")` |
 | "Is this bullish?" | `execute_python()` | Compute SMAs, trend, and analyze |
 | "Add Bollinger Bands" | `execute_python()` | Compute bands, use `plot_ohlc(df, title='BB')` |
 | "Find swing highs" | `execute_python()` | Use pandas logic to detect patterns |
 | "Plot ETH even though I'm viewing BTC" | `execute_python()` | Use `datasource_registry.get_source('binance')` to load ETH data |
 | "What indicators exist?" | `search_indicators()` | Search by category or query |
 | "What chart am I viewing?" | N/A - automatic | Chart info is in dynamic system prompt |
 | "Check my orders" | `execute_python()` | `print(order_store.orders)` |
 | "Read other stores" | `read_sync_state(store_name)` | For TraderState, StrategyState, etc. |
--- a/backend/memory/system_prompt.md
+++ b/backend/memory/system_prompt.md
@@ -59,140 +59,6 @@ You have access to:
 - Past strategy discussions and decisions
 - Relevant context retrieved automatically based on current conversation
 ## Important Behavioral Rules
 ### Chart Context Awareness
 When a user asks about "this chart", "the chart", "what I'm viewing", or similar references to their current view:
 1. **Chart info is automatically available** — The dynamic system prompt includes current chart state (symbol, interval, timeframe)
 2. **Check if chart is visible** — If ChartStore fields (symbol, interval) are `None`, the user is on a narrow screen (mobile) and no chart is visible
 3. **When chart is visible:**
   - **NEVER** ask the user to upload an image or tell you what symbol they're looking at
   - **Just use `execute_python()`** — It automatically loads the chart data from what they're viewing
   - Inside your Python script, `df` contains the data and `chart_context` has the metadata
   - Use `plot_ohlc(df)` to create beautiful candlestick charts
 4. **When chart is NOT visible (symbol is None):**
   - Let the user know they can view charts on a wider screen
   - You can still help with analysis using `get_historical_data()` if they specify a symbol
 This applies to questions like: "Can you see this chart?", "What are the swing highs and lows?", "Is this in an uptrend?", "What's the current price?", "Analyze this chart", "What am I looking at?"
 ### Data Analysis Workflow
 1. **Chart context is automatic** → Symbol, interval, and timeframe are in the dynamic system prompt (if chart is visible)
 2. **Check ChartStore** → If symbol/interval are `None`, no chart is visible (mobile view)
 3. **Use `execute_python()`** → This is your PRIMARY analysis tool
   - Automatically loads chart data into a pandas DataFrame `df` (if chart is visible)
   - Pre-imports numpy (`np`), pandas (`pd`), matplotlib (`plt`), and talib
   - Provides access to the indicator registry for computing indicators
   - Use `plot_ohlc(df)` helper for beautiful candlestick charts
 4. **Only use `get_chart_data()`** → For simple data inspection without analysis
 ### Python Analysis (`execute_python`) - Your Primary Tool
 **ALWAYS use `execute_python()` when the user asks for:**
 - Technical indicators (RSI, MACD, Bollinger Bands, moving averages, etc.)
 - Chart visualizations or plots
 - Statistical calculations or market analysis
 - Pattern detection or trend analysis
 - Any computational analysis of price data
 **Why `execute_python()` is preferred:**
 - Chart data (`df`) is automatically loaded from ChartStore (visible time range) when chart is visible
 - If no chart is visible (symbol is None), `df` will be None - but you can still load alternative data!
 - Full pandas/numpy/talib stack pre-imported
 - Use `plot_ohlc(df)` for instant professional candlestick charts
 - Access to 150+ indicators via `indicator_registry`
 - **Access to DataStores and registry** - order_store, chart_store, datasource_registry
 - **Can load ANY symbol/timeframe** using datasource_registry even when df is None
 - **Results include plots as image URLs** that are automatically displayed to the user
 - Prints and return values are included in the response
 **CRITICAL: Plots are automatically shown to the user**
 When you create a matplotlib figure (via `plot_ohlc()` or `plt.figure()`), it is automatically:
 1. Saved as a PNG image
 2. Returned in the response as a URL (e.g., `/uploads/plot_abc123.png`)
 3. **Displayed in the user's chat interface** - they see the image immediately
 You MUST use `execute_python()` with `plot_ohlc()` or matplotlib whenever the user wants to see a chart or plot.
 **IMPORTANT: Never use `get_historical_data()` for chart analysis**
 - `get_historical_data()` requires manual timestamp calculation and is only for custom queries
 - When analyzing what the user is viewing, ALWAYS use `execute_python()` which automatically loads the correct data
 - The `df` DataFrame in `execute_python()` is pre-loaded with the exact time range the user is viewing
 **Example workflows:**
 ```python
 # Computing an indicator and plotting (when chart is visible)
 execute_python("""
 df['RSI'] = talib.RSI(df['close'], 14)
 fig = plot_ohlc(df, title='Price with RSI')
 df[['close', 'RSI']].tail(10)
 """)
 # Multi-indicator analysis (when chart is visible)
 execute_python("""
 df['SMA20'] = df['close'].rolling(20).mean()
 df['BB_upper'] = df['close'].rolling(20).mean() + 2 * df['close'].rolling(20).std()
 df['BB_lower'] = df['close'].rolling(20).mean() - 2 * df['close'].rolling(20).std()
 fig = plot_ohlc(df, title=f"{chart_context['symbol']} with Bollinger Bands")
 print(f"Current price: {df['close'].iloc[-1]:.2f}")
 print(f"20-period SMA: {df['SMA20'].iloc[-1]:.2f}")
 """)
 # Loading alternative data (works even when chart not visible or for different symbols)
 execute_python("""
 from datetime import datetime, timedelta
 # Get data source
 binance = datasource_registry.get_source('binance')
 # Load data for any symbol/timeframe
 end_time = datetime.now()
 start_time = end_time - timedelta(days=7)
 result = await binance.get_history(
    symbol='ETH/USDT',
    interval='1h',
    start=int(start_time.timestamp()),
    end=int(end_time.timestamp())
 )
 # Convert to DataFrame
 rows = [{'time': pd.to_datetime(bar.time, unit='s'), **bar.data} for bar in result.bars]
 eth_df = pd.DataFrame(rows).set_index('time')
 # Analyze and plot
 eth_df['RSI'] = talib.RSI(eth_df['close'], 14)
 fig = plot_ohlc(eth_df, title='ETH/USDT 1h - RSI Analysis')
 print(f"ETH RSI: {eth_df['RSI'].iloc[-1]:.2f}")
 """)
 # Access stores to see current state
 execute_python("""
 print(f"Current symbol: {chart_store.chart_state.symbol}")
 print(f"Current interval: {chart_store.chart_state.interval}")
 print(f"Number of orders: {len(order_store.orders)}")
 """)
 ```
 **Only use `get_chart_data()` for:**
 - Quick inspection of raw bar data
 - When you just need the data structure without analysis
 ### Quick Reference: Common Tasks
 | User Request | Tool to Use | Example |
 |--------------|-------------|---------|
 | "Show me RSI" | `execute_python()` | `df['RSI'] = talib.RSI(df['close'], 14); plot_ohlc(df)` |
 | "What's the current price?" | `execute_python()` | `print(f"Current: {df['close'].iloc[-1]}")` |
 | "Is this bullish?" | `execute_python()` | Compute SMAs, trend, and analyze |
 | "Add Bollinger Bands" | `execute_python()` | Compute bands, use `plot_ohlc(df, title='BB')` |
 | "Find swing highs" | `execute_python()` | Use pandas logic to detect patterns |
 | "Plot ETH even though I'm viewing BTC" | `execute_python()` | Use `datasource_registry.get_source('binance')` to load ETH data |
 | "What indicators exist?" | `search_indicators()` | Search by category or query |
 | "What chart am I viewing?" | N/A - automatic | Chart info is in dynamic system prompt |
 | "Check my orders" | `execute_python()` | `print(order_store.orders)` |
 | "Read other stores" | `read_sync_state(store_name)` | For TraderState, StrategyState, etc. |
 ## Working with Users
 1. **Understand Intent**: Ask clarifying questions about strategy goals
@@ -200,3 +66,7 @@ print(f"Number of orders: {len(order_store.orders)}")
 3. **Validate**: Ensure strategy makes sense before generating code
 4. **Test**: Encourage backtesting and paper trading first
 5. **Monitor**: Help users interpret live strategy behavior
 ---
 **Note**: Additional context documents are loaded automatically to provide detailed operational guidelines. See memory files for specifics on chart context, shape drawing, Python analysis, and more.
--- a/backend/memory/tradingview_shapes.md
+++ b/backend/memory/tradingview_shapes.md
@@ -0,0 +1,612 @@
 # TradingView Shapes and Drawings Reference
 This document describes the various drawing shapes and studies available in TradingView charts, their properties, and control points. This information is useful for the AI agent to understand, create, and manipulate chart drawings.
 ## Shape Structure
 All shapes follow a common structure:
 - **id**: Unique identifier (string) - This is the TradingView-assigned ID after the shape is created
 - **type**: Shape type identifier (string)
 - **points**: Array of control points (each with `time` in Unix seconds and `price` as float)
 - **color**: Color as hex string (e.g., '#FF0000') or color name (e.g., 'red')
 - **line_width**: Line thickness in pixels (integer)
 - **line_style**: One of: 'solid', 'dashed', 'dotted'
 - **properties**: Dictionary of additional shape-specific properties
 - **symbol**: Trading pair symbol (e.g., 'BINANCE:BTC/USDT')
 - **created_at**: Creation timestamp (Unix seconds)
 - **modified_at**: Last modification timestamp (Unix seconds)
 - **original_id**: Optional string - The ID you requested when creating the shape, before TradingView assigned its own ID
 ## Understanding Shape ID Mapping
 When you create a shape using `create_or_update_shape()`, there's an important ID mapping process:
 1. **You specify an ID**: You provide a `shape_id` parameter (e.g., "my-support-line")
 2. **TradingView assigns its own ID**: When the shape is rendered in TradingView, it gets a new internal ID (e.g., "shape_0x1a2b3c4d")
 3. **ID remapping occurs**: The shape in the store is updated:
   - The `id` field becomes TradingView's ID
   - The `original_id` field preserves your requested ID
 4. **Tracking your shapes**: To find shapes you created, search by `original_id`
 ### Example ID Mapping Flow
 ```python
 # Step 1: Agent creates a shape with a specific ID
 await create_or_update_shape(
    shape_id="agent-support-50k",
    shape_type="horizontal_line",
    points=[{"time": 1678886400, "price": 50000}],
    color="#00FF00"
 )
 # Step 2: Shape is synced to client and created in TradingView
 # TradingView assigns ID: "shape_0x1a2b3c4d"
 # Step 3: Shape in store is updated with:
 # {
 #   "id": "shape_0x1a2b3c4d",           # TradingView's ID
 #   "original_id": "agent-support-50k",  # Your requested ID
 #   "type": "horizontal_line",
 #   ...
 # }
 # Step 4: To find your shape later, use shape_ids (searches both id and original_id)
 my_shapes = search_shapes(
    shape_ids=['agent-support-50k'],
    symbol="BINANCE:BTC/USDT"
 )
 if my_shapes:
    print(f"Found my support line!")
    print(f"TradingView ID: {my_shapes[0]['id']}")
    print(f"My requested ID: {my_shapes[0]['original_id']}")
 # Or use the dedicated original_ids parameter
 my_shapes = search_shapes(
    original_ids=['agent-support-50k'],
    symbol="BINANCE:BTC/USDT"
 )
 if my_shapes:
    print(f"Found my support line!")
    print(f"TradingView ID: {my_shapes[0]['id']}")
    print(f"My requested ID: {my_shapes[0]['original_id']}")
 ```
 ### Why ID Mapping Matters
 - **Shape identification**: You need to know which TradingView shape corresponds to the shape you created
 - **Updates and deletions**: To modify or delete a shape, you need its TradingView ID (the `id` field)
 - **Bidirectional sync**: The mapping ensures both the agent and TradingView can reference the same shape
 ### Best Practices for Shape IDs
 1. **Use descriptive IDs**: Choose meaningful names like `support-btc-50k` or `trendline-daily-uptrend`
 2. **Search by original ID**: Use `shape_ids` or `original_ids` parameters in `search_shapes()` to find your shapes
   - `shape_ids` searches both the actual ID and original_id (more flexible)
   - `original_ids` searches only the original_id field (more specific)
 3. **Store important IDs**: If you need to reference a shape multiple times, store its TradingView ID after retrieval
 4. **Understand the timing**: The ID remapping happens asynchronously after shape creation
 ## Common Shape Types
 Use TradingView's native shape type names directly.
 ### 1. Trendline
 **Type**: `trend_line`
 **Control Points**: 2
 - Point 1: Start of the line (time, price)
 - Point 2: End of the line (time, price)
 **Common Use Cases**:
 - Support/resistance lines
 - Trend identification
 - Price channels (when paired)
 **Example**:
 ```json
 {
  "id": "trendline-1",
  "type": "trend_line",
  "points": [
    {"time": 1640000000, "price": 45000.0},
    {"time": 1650000000, "price": 50000.0}
  ],
  "color": "#2962FF",
  "line_width": 2,
  "line_style": "solid"
 }
 ```
 ### 2. Horizontal Line
 **Type**: `horizontal_line`
 **Control Points**: 1
 - Point 1: Y-level (time can be any value, only price matters)
 **Common Use Cases**:
 - Support/resistance levels
 - Price targets
 - Stop-loss levels
 - Key psychological levels
 **Properties**:
 - `extend_left`: Boolean, extend line to the left
 - `extend_right`: Boolean, extend line to the right
 **Example**:
 ```json
 {
  "id": "support-1",
  "type": "horizontal_line",
  "points": [{"time": 1640000000, "price": 42000.0}],
  "color": "#089981",
  "line_width": 2,
  "line_style": "dashed",
  "properties": {
    "extend_left": true,
    "extend_right": true
  }
 }
 ```
 ### 3. Vertical Line
 **Type**: `vertical_line`
 **Control Points**: 1
 - Point 1: X-time (price can be any value, only time matters)
 **Common Use Cases**:
 - Mark important events
 - Session boundaries
 - Earnings releases
 - Economic data releases
 **Properties**:
 - `extend_top`: Boolean, extend line upward
 - `extend_bottom`: Boolean, extend line downward
 **Example**:
 ```json
 {
  "id": "event-marker-1",
  "type": "vertical_line",
  "points": [{"time": 1640000000, "price": 0}],
  "color": "#787B86",
  "line_width": 1,
  "line_style": "dotted"
 }
 ```
 ### 4. Rectangle
 **Type**: `rectangle`
 **Control Points**: 2
 - Point 1: Top-left corner (time, price)
 - Point 2: Bottom-right corner (time, price)
 **Common Use Cases**:
 - Consolidation zones
 - Support/resistance zones
 - Supply/demand areas
 - Value areas
 **Properties**:
 - `fill_color`: Fill color with opacity (e.g., '#2962FF33')
 - `fill`: Boolean, whether to fill the rectangle
 - `extend_left`: Boolean
 - `extend_right`: Boolean
 **Example**:
 ```json
 {
  "id": "zone-1",
  "type": "rectangle",
  "points": [
    {"time": 1640000000, "price": 50000.0},
    {"time": 1650000000, "price": 48000.0}
  ],
  "color": "#2962FF",
  "line_width": 1,
  "line_style": "solid",
  "properties": {
    "fill": true,
    "fill_color": "#2962FF33"
  }
 }
 ```
 ### 5. Fibonacci Retracement
 **Type**: `fib_retracement`
 **Control Points**: 2
 - Point 1: Start of the move (swing low or high)
 - Point 2: End of the move (swing high or low)
 **Common Use Cases**:
 - Identify potential support/resistance levels
 - Find retracement targets
 - Measure pullback depth
 **Properties**:
 - `levels`: Array of Fibonacci levels to display
  - Default: [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0]
 - `extend_lines`: Boolean, extend levels beyond the price range
 - `reverse`: Boolean, reverse the direction
 **Example**:
 ```json
 {
  "id": "fib-1",
  "type": "fib_retracement",
  "points": [
    {"time": 1640000000, "price": 42000.0},
    {"time": 1650000000, "price": 52000.0}
  ],
  "color": "#2962FF",
  "line_width": 1,
  "properties": {
    "levels": [0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0],
    "extend_lines": true
  }
 }
 ```
 ### 6. Fibonacci Extension
 **Type**: `fib_trend_ext`
 **Control Points**: 3
 - Point 1: Start of initial move
 - Point 2: End of initial move (retracement start)
 - Point 3: End of retracement
 **Common Use Cases**:
 - Project price targets
 - Extension levels beyond 100%
 - Measure continuation patterns
 **Properties**:
 - `levels`: Array of extension levels
  - Common: [0, 0.618, 1.0, 1.618, 2.618, 4.236]
 ### 7. Parallel Channel
 **Type**: `parallel_channel`
 **Control Points**: 3
 - Point 1: First point on main trendline
 - Point 2: Second point on main trendline
 - Point 3: Point on parallel line (determines channel width)
 **Common Use Cases**:
 - Price channels
 - Regression channels
 - Pitchforks
 **Properties**:
 - `extend_left`: Boolean
 - `extend_right`: Boolean
 - `fill`: Boolean, fill the channel
 - `fill_color`: Fill color with opacity
 ### 8. Arrow
 **Type**: `arrow`
 **Control Points**: 2
 - Point 1: Arrow start (time, price)
 - Point 2: Arrow end (time, price)
 **Common Use Cases**:
 - Indicate price movement direction
 - Mark entry/exit points
 - Show relationships between events
 **Properties**:
 - `arrow_style`: One of: 'simple', 'filled', 'hollow'
 - `text`: Optional text label
 **Example**:
 ```json
 {
  "id": "entry-arrow",
  "type": "arrow",
  "points": [
    {"time": 1640000000, "price": 44000.0},
    {"time": 1641000000, "price": 48000.0}
  ],
  "color": "#089981",
  "line_width": 2,
  "properties": {
    "arrow_style": "filled",
    "text": "Long Entry"
  }
 }
 ```
 ### 9. Text/Label
 **Type**: `text`
 **Control Points**: 1
 - Point 1: Text anchor position (time, price)
 **Common Use Cases**:
 - Annotations
 - Notes
 - Labels for patterns
 - Mark key levels
 **Properties**:
 - `text`: The text content (string)
 - `font_size`: Font size in points (integer)
 - `font_family`: Font family name
 - `bold`: Boolean
 - `italic`: Boolean
 - `background`: Boolean, show background
 - `background_color`: Background color
 - `text_color`: Text color (can differ from line color)
 **Example**:
 ```json
 {
  "id": "note-1",
  "type": "text",
  "points": [{"time": 1640000000, "price": 48000.0}],
  "color": "#131722",
  "properties": {
    "text": "Resistance Zone",
    "font_size": 14,
    "bold": true,
    "background": true,
    "background_color": "#FFE600",
    "text_color": "#131722"
  }
 }
 ```
 ### 10. Single-Point Markers
 Various single-point marker shapes are available for annotating charts:
 **Types**: `arrow_up` | `arrow_down` | `flag` | `emoji` | `icon` | `sticker` | `note` | `anchored_text` | `anchored_note` | `long_position` | `short_position`
 **Control Points**: 1
 - Point 1: Marker position (time, price)
 **Common Use Cases**:
 - Mark entry/exit points
 - Flag important events
 - Add visual markers to key levels
 - Annotate patterns
 - Track positions
 **Properties** (vary by type):
 - `text`: Text content for text-based markers
 - `emoji`: Emoji character for emoji type
 - `icon`: Icon identifier for icon type
 **Examples**:
 ```json
 {
  "id": "long-entry-1",
  "type": "long_position",
  "points": [{"time": 1640000000, "price": 44000.0}],
  "color": "#089981"
 }
 ```
 ```json
 {
  "id": "flag-1",
  "type": "flag",
  "points": [{"time": 1640000000, "price": 50000.0}],
  "color": "#F23645",
  "properties": {
    "text": "Important Event"
  }
 }
 ```
 ```json
 {
  "id": "note-1",
  "type": "anchored_note",
  "points": [{"time": 1640000000, "price": 48000.0}],
  "color": "#FFE600",
  "properties": {
    "text": "Watch this level"
  }
 }
 ```
 ### 11. Circle/Ellipse
 **Type**: `circle`
 **Control Points**: 2 or 3
 - 2 points: Defines bounding box (creates ellipse)
 - 3 points: Center + radius points
 **Common Use Cases**:
 - Highlight areas
 - Markup patterns
 - Mark consolidation zones
 **Properties**:
 - `fill`: Boolean
 - `fill_color`: Fill color with opacity
 ### 12. Path (Free Drawing)
 **Type**: `path`
 **Control Points**: Variable (3+)
 - Multiple points defining a path
 **Common Use Cases**:
 - Custom patterns
 - Freeform markup
 - Complex annotations
 **Properties**:
 - `closed`: Boolean, whether to close the path
 - `smooth`: Boolean, smooth the path with curves
 ### 13. Pitchfork (Andrew's Pitchfork)
 **Type**: `pitchfork`
 **Control Points**: 3
 - Point 1: Pivot/starting point
 - Point 2: First extreme (high or low)
 - Point 3: Second extreme (opposite of point 2)
 **Common Use Cases**:
 - Trend channels
 - Support/resistance levels
 - Median line analysis
 **Properties**:
 - `extend_lines`: Boolean
 - `style`: One of: 'standard', 'schiff', 'modified_schiff'
 ### 14. Gann Fan
 **Type**: `gannbox_fan`
 **Control Points**: 2
 - Point 1: Origin point
 - Point 2: Defines the unit size/scale
 **Common Use Cases**:
 - Time and price analysis
 - Geometric angles (1x1, 1x2, 2x1, etc.)
 **Properties**:
 - `angles`: Array of angles to display
  - Default: [82.5, 75, 71.25, 63.75, 45, 26.25, 18.75, 15, 7.5]
 ### 15. Head and Shoulders
 **Type**: `head_and_shoulders`
 **Control Points**: 5
 - Point 1: Left shoulder low
 - Point 2: Left shoulder high
 - Point 3: Head low
 - Point 4: Right shoulder high
 - Point 5: Right shoulder low (neckline point)
 **Common Use Cases**:
 - Pattern recognition markup
 - Reversal pattern identification
 **Properties**:
 - `target_line`: Boolean, show target line
 ## Special Properties
 ### Time-Based Properties
 - All times are Unix timestamps in seconds
 - Use `Math.floor(Date.now() / 1000)` for current time in JavaScript
 - Use `int(time.time())` for current time in Python
 ### Color Formats
 - Hex: `#RRGGBB` (e.g., `#2962FF`)
 - Hex with alpha: `#RRGGBBAA` (e.g., `#2962FF33` for 20% opacity)
 - Named colors: `red`, `blue`, `green`, etc.
 - RGB: `rgb(41, 98, 255)`
 - RGBA: `rgba(41, 98, 255, 0.2)`
 ### Line Styles
 - `solid`: Continuous line
 - `dashed`: Dashed line (— — —)
 - `dotted`: Dotted line (· · ·)
 ## Best Practices
 1. **ID Naming**: Use descriptive IDs that indicate the purpose
   - Good: `support-btc-42k`, `trendline-uptrend-1`
   - Bad: `shape1`, `line`
 2. **Color Consistency**: Use consistent colors for similar types
   - Green (#089981) for bullish/support
   - Red (#F23645) for bearish/resistance
   - Blue (#2962FF) for neutral/informational
 3. **Time Alignment**: Ensure times align with actual candles when possible
 4. **Layer Management**: Use different line widths to indicate importance
   - Key levels: 2-3px
   - Secondary levels: 1px
   - Reference lines: 1px dotted
 5. **Symbol Association**: Always set the `symbol` field to associate shapes with specific charts
 ## Agent Usage Examples
 ### Drawing a Support Level
 When user says "draw support at 42000":
 ```python
 await create_or_update_shape(
    shape_id=f"support-{int(time.time())}",
    shape_type='horizontal_line',
    points=[{'time': current_time, 'price': 42000.0}],
    color='#089981',
    line_width=2,
    line_style='solid',
    symbol=chart_store.chart_state.symbol,
    properties={'extend_left': True, 'extend_right': True}
 )
 ```
 ### Finding Shapes in Visible Range
 When user asks "what drawings are on the chart?":
 ```python
 shapes = search_shapes(
    start_time=chart_store.chart_state.start_time,
    end_time=chart_store.chart_state.end_time,
    symbol=chart_store.chart_state.symbol
 )
 ```
 ### Getting Specific Shapes by ID
 When user says "show me the details of trendline-1":
 ```python
 # shape_ids parameter searches BOTH the actual ID and original_id fields
 shapes = search_shapes(
    shape_ids=['trendline-1']
 )
 ```
 Or to get selected shapes:
 ```python
 selected_ids = chart_store.chart_state.selected_shapes
 if selected_ids:
    shapes = search_shapes(shape_ids=selected_ids)
 ```
 ### Finding Shapes by Original ID
 When you need to find shapes you created using the original ID you specified:
 ```python
 # Use the dedicated original_ids parameter
 my_shapes = search_shapes(
    original_ids=['my-support-line', 'my-trendline']
 )
 # Or use shape_ids (which searches both id and original_id)
 my_shapes = search_shapes(
    shape_ids=['my-support-line', 'my-trendline']
 )
 for shape in my_shapes:
    print(f"Original ID: {shape['original_id']}")
    print(f"TradingView ID: {shape['id']}")
    print(f"Type: {shape['type']}")
 ```
 ### Searching Without Time Filter
 When user asks "show me all support lines":
 ```python
 support_lines = search_shapes(
    shape_type='horizontal_line',
    symbol=chart_store.chart_state.symbol
 )
 ```
 ### Drawing a Trendline
 When user says "draw an uptrend from the lows":
 ```python
 # Find swing lows using execute_python
 # Then create trendline
 await create_or_update_shape(
    shape_id=f"trendline-{int(time.time())}",
    shape_type='trend_line',
    points=[
        {'time': swing_low_1_time, 'price': swing_low_1_price},
        {'time': swing_low_2_time, 'price': swing_low_2_price}
    ],
    color='#2962FF',
    line_width=2,
    symbol=chart_store.chart_state.symbol
 )
 ```
--- a/backend/src/agent/core.py
+++ b/backend/src/agent/core.py
@@ -7,7 +7,7 @@ from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
 from langchain_core.runnables import RunnableConfig
 from langgraph.prebuilt import create_react_agent
-from agent.tools import SYNC_TOOLS, DATASOURCE_TOOLS, INDICATOR_TOOLS, RESEARCH_TOOLS, CHART_TOOLS
+from agent.tools import SYNC_TOOLS, DATASOURCE_TOOLS, INDICATOR_TOOLS, RESEARCH_TOOLS, CHART_TOOLS, SHAPE_TOOLS
 from agent.memory import MemoryManager
 from agent.session import SessionManager
 from agent.prompts import build_system_prompt
@@ -65,10 +65,10 @@ class AgentExecutor:
        # Create agent without a static system prompt
        # We'll pass the dynamic system prompt via state_modifier at runtime
-        # Include all tool categories: sync, datasource, chart, indicator, and research
+        # Include all tool categories: sync, datasource, chart, indicator, shape, and research
        self.agent = create_react_agent(
            self.llm,
-            SYNC_TOOLS + DATASOURCE_TOOLS + CHART_TOOLS + INDICATOR_TOOLS + RESEARCH_TOOLS,
+            SYNC_TOOLS + DATASOURCE_TOOLS + CHART_TOOLS + INDICATOR_TOOLS + SHAPE_TOOLS + RESEARCH_TOOLS,
            checkpointer=checkpointer
        )
--- a/backend/src/agent/prompts.py
+++ b/backend/src/agent/prompts.py
@@ -30,6 +30,11 @@ def _get_chart_store_context() -> str:
        interval = chart_data.get("interval", "N/A")
        start_time = chart_data.get("start_time")
        end_time = chart_data.get("end_time")
        selected_shapes = chart_data.get("selected_shapes", [])
        selected_info = ""
        if selected_shapes:
            selected_info = f"\n- **Selected Shapes**: {len(selected_shapes)} shape(s) selected (IDs: {', '.join(selected_shapes)})"
        chart_context = f"""
 ## Current Chart Context
@@ -37,7 +42,7 @@ def _get_chart_store_context() -> str:
 The user is currently viewing a chart with the following settings:
 - **Symbol**: {symbol}
 - **Interval**: {interval}
- **Time Range**: {f"from {start_time} to {end_time}" if start_time and end_time else "not set"}
+- **Time Range**: {f"from {start_time} to {end_time}" if start_time and end_time else "not set"}{selected_info}
 This information is automatically available because you're connected via websocket.
 When the user refers to "the chart", "this chart", or "what I'm viewing", this is what they mean.
--- a/backend/src/agent/tools/init.py
+++ b/backend/src/agent/tools/init.py
@@ -5,6 +5,7 @@ This package provides tools for:
 - Data sources and market data (datasource_tools)
 - Chart data access and analysis (chart_tools)
 - Technical indicators (indicator_tools)
 - Shape/drawing management (shape_tools)
 """
 # Global registries that will be set by main.py
@@ -37,6 +38,7 @@ from .datasource_tools import DATASOURCE_TOOLS
 from .chart_tools import CHART_TOOLS
 from .indicator_tools import INDICATOR_TOOLS
 from .research_tools import RESEARCH_TOOLS
 from .shape_tools import SHAPE_TOOLS
 __all__ = [
    "set_registry",
@@ -47,4 +49,5 @@ __all__ = [
    "CHART_TOOLS",
    "INDICATOR_TOOLS",
    "RESEARCH_TOOLS",
    "SHAPE_TOOLS",
 ]
--- a/backend/src/agent/tools/shape_tools.py
+++ b/backend/src/agent/tools/shape_tools.py
@@ -0,0 +1,475 @@
 """Shape/drawing tools for chart analysis."""
 from typing import Dict, Any, List, Optional
 from langchain_core.tools import tool
 import logging
 logger = logging.getLogger(__name__)
 # Map legacy/common shape type names to TradingView's native names
 SHAPE_TYPE_ALIASES: Dict[str, str] = {
    'trendline': 'trend_line',
    'fibonacci': 'fib_retracement',
    'fibonacci_extension': 'fib_trend_ext',
    'gann_fan': 'gannbox_fan',
 }
 def _get_registry():
    """Get the global registry instance."""
    from . import _registry
    return _registry
 def _get_shape_store():
    """Get the global ShapeStore instance."""
    registry = _get_registry()
    if registry and "ShapeStore" in registry.entries:
        return registry.entries["ShapeStore"].model
    return None
@tool
 def search_shapes(
    start_time: Optional[int] = None,
    end_time: Optional[int] = None,
    shape_type: Optional[str] = None,
    symbol: Optional[str] = None,
    shape_ids: Optional[List[str]] = None,
    original_ids: Optional[List[str]] = None
 ) -> List[Dict[str, Any]]:
    """Search for shapes/drawings using flexible filters.
    This tool can search shapes by:
    - Time range (finds shapes that overlap the range)
    - Shape type (e.g., 'trendline', 'horizontal_line')
    - Symbol (e.g., 'BINANCE:BTC/USDT')
    - Specific shape IDs (TradingView's assigned IDs)
    - Original IDs (the IDs you specified when creating shapes)
    Args:
        start_time: Optional start of time range (Unix timestamp in seconds)
        end_time: Optional end of time range (Unix timestamp in seconds)
        shape_type: Optional filter by shape type (e.g., 'trend_line', 'horizontal_line', 'rectangle')
        symbol: Optional filter by symbol (e.g., 'BINANCE:BTC/USDT')
        shape_ids: Optional list of specific shape IDs to retrieve (searches both id and original_id fields)
        original_ids: Optional list of original IDs to search for (the IDs you specified when creating)
    Returns:
        List of matching shapes, each as a dictionary with:
        - id: Shape identifier (TradingView's assigned ID)
        - original_id: The ID you specified when creating the shape (if applicable)
        - type: Shape type
        - points: List of control points with time and price
        - color, line_width, line_style: Visual properties
        - properties: Additional shape-specific properties
        - symbol: Symbol the shape is drawn on
        - created_at, modified_at: Timestamps
    Examples:
        # Find all shapes in the currently visible chart range
        shapes = search_shapes(
            start_time=chart_state.start_time,
            end_time=chart_state.end_time
        )
        # Find only trendlines in a specific time range
        trendlines = search_shapes(
            start_time=1640000000,
            end_time=1650000000,
            shape_type='trend_line'
        )
        # Find shapes for a specific symbol
        btc_shapes = search_shapes(
            start_time=1640000000,
            end_time=1650000000,
            symbol='BINANCE:BTC/USDT'
        )
        # Get specific shapes by TradingView ID or original ID
        # This searches both the 'id' and 'original_id' fields
        selected = search_shapes(
            shape_ids=['trendline-1', 'support-42k', 'fib-retracement-1']
        )
        # Get shapes by the original IDs you specified when creating them
        my_shapes = search_shapes(
            original_ids=['my-support-line', 'my-resistance-line']
        )
        # Get all trendlines (no time filter)
        all_trendlines = search_shapes(shape_type='trend_line')
    """
    shape_store = _get_shape_store()
    if not shape_store:
        raise ValueError("ShapeStore not initialized")
    shapes_dict = shape_store.shapes
    matching_shapes = []
    # If specific shape IDs are requested, search by both id and original_id
    if shape_ids:
        for requested_id in shape_ids:
            # First try direct ID lookup
            shape = shapes_dict.get(requested_id)
            if shape:
                # Still apply other filters if specified
                if symbol and shape.get('symbol') != symbol:
                    continue
                if shape_type and shape.get('type') != shape_type:
                    continue
                matching_shapes.append(shape)
            else:
                # If not found by ID, search by original_id
                for shape_id, shape in shapes_dict.items():
                    if shape.get('original_id') == requested_id:
                        # Still apply other filters if specified
                        if symbol and shape.get('symbol') != symbol:
                            continue
                        if shape_type and shape.get('type') != shape_type:
                            continue
                        matching_shapes.append(shape)
                        break
        logger.info(
            f"Found {len(matching_shapes)} shapes by ID filter (requested {len(shape_ids)} IDs)"
            + (f" for type '{shape_type}'" if shape_type else "")
            + (f" on symbol '{symbol}'" if symbol else "")
        )
        return matching_shapes
    # If specific original IDs are requested, search by original_id only
    if original_ids:
        for original_id in original_ids:
            for shape_id, shape in shapes_dict.items():
                if shape.get('original_id') == original_id:
                    # Still apply other filters if specified
                    if symbol and shape.get('symbol') != symbol:
                        continue
                    if shape_type and shape.get('type') != shape_type:
                        continue
                    matching_shapes.append(shape)
                    break
        logger.info(
            f"Found {len(matching_shapes)} shapes by original_id filter (requested {len(original_ids)} IDs)"
            + (f" for type '{shape_type}'" if shape_type else "")
            + (f" on symbol '{symbol}'" if symbol else "")
        )
        return matching_shapes
    # Otherwise, search all shapes with filters
    for shape_id, shape in shapes_dict.items():
        # Filter by symbol if specified
        if symbol and shape.get('symbol') != symbol:
            continue
        # Filter by type if specified
        if shape_type and shape.get('type') != shape_type:
            continue
        # Filter by time range if specified
        if start_time is not None and end_time is not None:
            # Check if any control point falls within the time range
            # or if the shape spans across the time range
            points = shape.get('points', [])
            if not points:
                continue
            # Get min and max times from shape's control points
            shape_times = [point['time'] for point in points]
            shape_min_time = min(shape_times)
            shape_max_time = max(shape_times)
            # Check for overlap: shape overlaps if its range intersects with query range
            if not (shape_max_time >= start_time and shape_min_time <= end_time):
                continue
        matching_shapes.append(shape)
    logger.info(
        f"Found {len(matching_shapes)} shapes"
        + (f" in time range {start_time}-{end_time}" if start_time and end_time else "")
        + (f" for type '{shape_type}'" if shape_type else "")
        + (f" on symbol '{symbol}'" if symbol else "")
    )
    return matching_shapes
@tool
 async def create_or_update_shape(
    shape_id: str,
    shape_type: str,
    points: List[Dict[str, Any]],
    color: Optional[str] = None,
    line_width: Optional[int] = None,
    line_style: Optional[str] = None,
    properties: Optional[Dict[str, Any]] = None,
    symbol: Optional[str] = None
 ) -> Dict[str, Any]:
    """Create a new shape or update an existing shape on the chart.
    This tool allows the agent to draw shapes on the user's chart or modify
    existing shapes. Shapes are synchronized to the frontend in real-time.
    IMPORTANT - Shape ID Mapping:
    When you create a shape, TradingView will assign its own internal ID that differs
    from the shape_id you provide. The shape will be updated in the store with:
    - id: TradingView's assigned ID
    - original_id: The shape_id you provided
    To find your shape later, use search_shapes() and filter by original_id field.
    Example:
        # Create a shape
        await create_or_update_shape(shape_id='my-support', ...)
        # Later, find it by original_id
        shapes = search_shapes(symbol='BINANCE:BTC/USDT')
        my_shape = next((s for s in shapes if s.get('original_id') == 'my-support'), None)
    Args:
        shape_id: Unique identifier for the shape (use existing ID to update, new ID to create)
                  Note: TradingView will assign its own ID; your ID will be stored in original_id
        shape_type: Type of shape using TradingView's native names.
                    Single-point shapes (use 1 point):
                    - 'horizontal_line': Horizontal support/resistance line
                    - 'vertical_line': Vertical time marker
                    - 'text': Text label
                    - 'anchored_text': Anchored text annotation
                    - 'anchored_note': Anchored note
                    - 'note': Note annotation
                    - 'emoji': Emoji marker
                    - 'icon': Icon marker
                    - 'sticker': Sticker marker
                    - 'arrow_up': Upward arrow marker
                    - 'arrow_down': Downward arrow marker
                    - 'flag': Flag marker
                    - 'long_position': Long position marker
                    - 'short_position': Short position marker
                    Multi-point shapes (use 2+ points):
                    - 'trend_line': Trendline (2 points)
                    - 'rectangle': Rectangle (2 points: top-left, bottom-right)
                    - 'fib_retracement': Fibonacci retracement (2 points)
                    - 'fib_trend_ext': Fibonacci extension (3 points)
                    - 'parallel_channel': Parallel channel (3 points)
                    - 'arrow': Arrow (2 points)
                    - 'circle': Circle/ellipse (2-3 points)
                    - 'path': Free drawing path (3+ points)
                    - 'pitchfork': Andrew's pitchfork (3 points)
                    - 'gannbox_fan': Gann fan (2 points)
                    - 'head_and_shoulders': Head and shoulders pattern (5 points)
        points: List of control points, each with 'time' (Unix seconds) and 'price' fields
        color: Optional color (hex like '#FF0000' or name like 'red')
        line_width: Optional line width in pixels (default: 1)
        line_style: Optional line style: 'solid', 'dashed', 'dotted' (default: 'solid')
        properties: Optional dict of additional shape-specific properties
        symbol: Optional symbol to associate with the shape (defaults to current chart symbol)
    Returns:
        Dictionary with:
        - status: 'created' or 'updated'
        - shape: The complete shape object (initially with your ID, will be updated to TV ID)
    Examples:
        # Draw a trendline between two points
        await create_or_update_shape(
            shape_id='my-trendline-1',
            shape_type='trend_line',
            points=[
                {'time': 1640000000, 'price': 45000.0},
                {'time': 1650000000, 'price': 50000.0}
            ],
            color='#00FF00',
            line_width=2
        )
        # Draw a horizontal support line
        await create_or_update_shape(
            shape_id='support-1',
            shape_type='horizontal_line',
            points=[{'time': 1640000000, 'price': 42000.0}],
            color='blue',
            line_style='dashed'
        )
        # Find your shape after creation using original_id
        shapes = search_shapes(symbol='BINANCE:BTC/USDT')
        my_shape = next((s for s in shapes if s.get('original_id') == 'support-1'), None)
        if my_shape:
            print(f"TradingView assigned ID: {my_shape['id']}")
    """
    from schema.shape import Shape, ControlPoint
    import time as time_module
    registry = _get_registry()
    if not registry:
        raise ValueError("SyncRegistry not initialized")
    shape_store = _get_shape_store()
    if not shape_store:
        raise ValueError("ShapeStore not initialized")
    # Normalize shape type (handle legacy names)
    normalized_type = SHAPE_TYPE_ALIASES.get(shape_type, shape_type)
    if normalized_type != shape_type:
        logger.info(f"Normalized shape type '{shape_type}' -> '{normalized_type}'")
    # Convert points to ControlPoint objects
    control_points = []
    for p in points:
        point_data = {
            'time': p['time'],
            'price': p['price']
        }
        # Only include channel if it's actually provided
        if 'channel' in p and p['channel'] is not None:
            point_data['channel'] = p['channel']
        control_points.append(ControlPoint(**point_data))
    # Check if updating existing shape
    existing_shape = shape_store.shapes.get(shape_id)
    is_update = existing_shape is not None
    # If symbol is not provided, try to get it from ChartStore
    if symbol is None and "ChartStore" in registry.entries:
        chart_store = registry.entries["ChartStore"].model
        if hasattr(chart_store, 'chart_state') and hasattr(chart_store.chart_state, 'symbol'):
            symbol = chart_store.chart_state.symbol
            logger.info(f"Using current chart symbol for shape: {symbol}")
    now = int(time_module.time())
    # Create shape object
    shape = Shape(
        id=shape_id,
        type=normalized_type,
        points=control_points,
        color=color,
        line_width=line_width,
        line_style=line_style,
        properties=properties or {},
        symbol=symbol,
        created_at=existing_shape.get('created_at') if existing_shape else now,
        modified_at=now
    )
    # Update the store
    shape_store.shapes[shape_id] = shape.model_dump(mode="json")
    # Trigger sync
    await registry.push_all()
    logger.info(
        f"{'Updated' if is_update else 'Created'} shape '{shape_id}' "
        f"of type '{shape_type}' with {len(points)} points"
    )
    return {
        "status": "updated" if is_update else "created",
        "shape": shape.model_dump(mode="json")
    }
@tool
 async def delete_shape(shape_id: str) -> Dict[str, str]:
    """Delete a shape from the chart.
    Args:
        shape_id: ID of the shape to delete
    Returns:
        Dictionary with status message
    Raises:
        ValueError: If shape doesn't exist
    Example:
        await delete_shape('my-trendline-1')
    """
    registry = _get_registry()
    if not registry:
        raise ValueError("SyncRegistry not initialized")
    shape_store = _get_shape_store()
    if not shape_store:
        raise ValueError("ShapeStore not initialized")
    if shape_id not in shape_store.shapes:
        raise ValueError(f"Shape '{shape_id}' not found")
    # Delete the shape
    del shape_store.shapes[shape_id]
    # Trigger sync
    await registry.push_all()
    logger.info(f"Deleted shape '{shape_id}'")
    return {
        "status": "success",
        "message": f"Shape '{shape_id}' deleted"
    }
@tool
 def get_shape(shape_id: str) -> Dict[str, Any]:
    """Get details of a specific shape by ID.
    Args:
        shape_id: ID of the shape to retrieve
    Returns:
        Dictionary containing the shape data
    Raises:
        ValueError: If shape doesn't exist
    Example:
        shape = get_shape('my-trendline-1')
        print(f"Shape type: {shape['type']}")
        print(f"Points: {shape['points']}")
    """
    shape_store = _get_shape_store()
    if not shape_store:
        raise ValueError("ShapeStore not initialized")
    shape = shape_store.shapes.get(shape_id)
    if not shape:
        raise ValueError(f"Shape '{shape_id}' not found")
    return shape
@tool
 def list_all_shapes() -> List[Dict[str, Any]]:
    """List all shapes currently on the chart.
    Returns:
        List of all shapes as dictionaries
    Example:
        shapes = list_all_shapes()
        print(f"Total shapes: {len(shapes)}")
        for shape in shapes:
            print(f"  - {shape['id']}: {shape['type']}")
    """
    shape_store = _get_shape_store()
    if not shape_store:
        raise ValueError("ShapeStore not initialized")
    return list(shape_store.shapes.values())
 SHAPE_TOOLS = [
    search_shapes,
    create_or_update_shape,
    delete_shape,
    get_shape,
    list_all_shapes
 ]
--- a/backend/src/main.py
+++ b/backend/src/main.py
@@ -23,6 +23,7 @@ from agent.core import create_agent
 from agent.tools import set_registry, set_datasource_registry, set_indicator_registry
 from schema.order_spec import SwapOrder
 from schema.chart_state import ChartState
 from schema.shape import ShapeCollection
 from datasource.registry import DataSourceRegistry
 from datasource.subscription_manager import SubscriptionManager
 from datasource.websocket_handler import DatafeedWebSocketHandler
@@ -124,7 +125,7 @@ async def lifespan(app: FastAPI):
            chroma_db_path=config["memory"]["chroma_db"],
            embedding_model=config["memory"]["embedding_model"],
            context_docs_dir=config["agent"]["context_docs_dir"],
-            base_dir=".."  # Point to project root from backend/src
+            base_dir="."  # backend/src is the working directory, so . goes to backend, where memory/ lives
        )
        await agent_executor.initialize()
@@ -159,13 +160,19 @@ class OrderStore(BaseModel):
 class ChartStore(BaseModel):
    chart_state: ChartState = ChartState()
 # ShapeStore model for synchronization
 class ShapeStore(BaseModel):
    shapes: dict[str, dict] = {}  # Dictionary of shapes keyed by ID
 # Initialize stores
 order_store = OrderStore()
 chart_store = ChartStore()
 shape_store = ShapeStore()
 # Register with SyncRegistry
 registry.register(order_store, store_name="OrderStore")
 registry.register(chart_store, store_name="ChartStore")
 registry.register(shape_store, store_name="ShapeStore")
@app.websocket("/ws")
 async def websocket_endpoint(websocket: WebSocket):
@@ -361,11 +368,14 @@ async def websocket_endpoint(websocket: WebSocket):
            elif msg_type == "patch":
                patch_msg = PatchMessage(**message_json)
                logger.info(f"Patch message received for store: {patch_msg.store}, seq: {patch_msg.seq}")
-                await registry.apply_client_patch(
+                try:
-                    store_name=patch_msg.store,
+                    await registry.apply_client_patch(
-                    client_base_seq=patch_msg.seq,
+                        store_name=patch_msg.store,
-                    patch=patch_msg.patch
+                        client_base_seq=patch_msg.seq,
-                )
+                        patch=patch_msg.patch
                    )
                except Exception as e:
                    logger.error(f"Error applying client patch: {e}. Client will receive snapshot to resync.", exc_info=True)
            elif msg_type == "agent_user_message":
                # Handle agent messages directly here
                print(f"[DEBUG] Raw message_json: {message_json}")
--- a/backend/src/schema/chart_state.py
+++ b/backend/src/schema/chart_state.py
@@ -1,4 +1,4 @@
-from typing import Optional
+from typing import Optional, List
 from pydantic import BaseModel, Field
@@ -23,3 +23,6 @@ class ChartState(BaseModel):
    # Optional: Chart interval/resolution
    # None when chart is not visible
    interval: Optional[str] = Field(default="15", description="Chart interval (e.g., '1', '5', '15', '60', 'D'), or None if no chart visible")
    # Selected shapes/drawings on the chart
    selected_shapes: List[str] = Field(default_factory=list, description="Array of selected shape IDs")
--- a/backend/src/schema/shape.py
+++ b/backend/src/schema/shape.py
@@ -0,0 +1,44 @@
 from typing import List, Dict, Any, Optional
 from pydantic import BaseModel, Field
 class ControlPoint(BaseModel):
    """A control point for a drawing shape.
    Control points define the position and properties of a shape.
    Different shapes have different numbers of control points.
    """
    time: int = Field(..., description="Unix timestamp in seconds")
    price: float = Field(..., description="Price level")
    # Optional channel for multi-point shapes (e.g., parallel channels)
    channel: Optional[str] = Field(default=None, description="Channel identifier for multi-point shapes")
 class Shape(BaseModel):
    """A TradingView drawing shape/study.
    Represents any drawing the user creates on the chart (trendlines,
    horizontal lines, rectangles, Fibonacci retracements, etc.)
    """
    id: str = Field(..., description="Unique identifier for the shape")
    type: str = Field(..., description="Shape type (e.g., 'trendline', 'horizontal_line', 'rectangle', 'fibonacci')")
    points: List[ControlPoint] = Field(default_factory=list, description="Control points that define the shape")
    # Visual properties
    color: Optional[str] = Field(default=None, description="Shape color (hex or color name)")
    line_width: Optional[int] = Field(default=1, description="Line width in pixels")
    line_style: Optional[str] = Field(default="solid", description="Line style: 'solid', 'dashed', 'dotted'")
    # Shape-specific properties stored as flexible dict
    properties: Dict[str, Any] = Field(default_factory=dict, description="Additional shape-specific properties")
    # Metadata
    symbol: Optional[str] = Field(default=None, description="Symbol this shape is drawn on")
    created_at: Optional[int] = Field(default=None, description="Creation timestamp (Unix seconds)")
    modified_at: Optional[int] = Field(default=None, description="Last modification timestamp (Unix seconds)")
    original_id: Optional[str] = Field(default=None, description="Original ID from backend/agent before TradingView assigns its own ID")
 class ShapeCollection(BaseModel):
    """Collection of all shapes/drawings on the chart."""
    shapes: Dict[str, Shape] = Field(default_factory=dict, description="Dictionary of shapes keyed by ID")
--- a/backend/src/sync/registry.py
+++ b/backend/src/sync/registry.py
@@ -105,67 +105,105 @@ class SyncRegistry:
        logger.info(f"apply_client_patch: Current backend seq={entry.seq}")
-        if client_base_seq == entry.seq:
+        try:
-            # No conflict
+            if client_base_seq == entry.seq:
-            logger.info("apply_client_patch: No conflict - applying patch directly")
+                # No conflict
-            current_state = entry.model.model_dump(mode="json")
+                logger.info("apply_client_patch: No conflict - applying patch directly")
-            logger.info(f"apply_client_patch: Current state before patch: {current_state}")
+                current_state = entry.model.model_dump(mode="json")
-            new_state = jsonpatch.apply_patch(current_state, patch)
+                logger.info(f"apply_client_patch: Current state before patch: {current_state}")
-            logger.info(f"apply_client_patch: New state after patch: {new_state}")
+                try:
-            self._update_model(entry.model, new_state)
+                    new_state = jsonpatch.apply_patch(current_state, patch)
                    logger.info(f"apply_client_patch: New state after patch: {new_state}")
                    self._update_model(entry.model, new_state)
-            entry.commit_patch(patch)
+                    entry.commit_patch(patch)
-            logger.info(f"apply_client_patch: Patch committed, new seq={entry.seq}")
+                    logger.info(f"apply_client_patch: Patch committed, new seq={entry.seq}")
-            # Don't broadcast back to client - they already have this change
+                    # Don't broadcast back to client - they already have this change
-            # Broadcasting would cause an infinite loop
+                    # Broadcasting would cause an infinite loop
-            logger.info("apply_client_patch: Not broadcasting back to originating client")
+                    logger.info("apply_client_patch: Not broadcasting back to originating client")
-        
+                except jsonpatch.JsonPatchConflict as e:
-        elif client_base_seq < entry.seq:
+                    logger.warning(f"apply_client_patch: Patch conflict on no-conflict path: {e}. Sending snapshot to resync.")
-            # Conflict! Frontend wins.
+                    # Send snapshot to force resync
-            # 1. Get backend patches since client_base_seq
+                    if self.websocket:
-            backend_patches = []
+                        msg = SnapshotMessage(
-            for seq, p in entry.history:
+                            store=entry.store_name,
-                if seq > client_base_seq:
+                            seq=entry.seq,
-                    backend_patches.append(p)
+                            state=entry.model.model_dump(mode="json")
-            
+                        )
-            # 2. Apply frontend patch first to the state at client_base_seq
+                        await self.websocket.send_json(msg.model_dump(mode="json"))
-            # But we only have the current authoritative model.
+
-            # "Apply the frontend patch first to the model (frontend wins)"
+            elif client_base_seq < entry.seq:
-            # "Re-apply the backend deltas that do not overlap the frontend's changed paths on top"
+                # Conflict! Frontend wins.
-            
+                # 1. Get backend patches since client_base_seq
-            # Let's get the state as it was at client_base_seq if possible? 
+                backend_patches = []
-            # No, history only has patches.
+                for seq, p in entry.history:
-            
+                    if seq > client_base_seq:
-            # Alternative: Apply frontend patch to current model. 
+                        backend_patches.append(p)
-            # Then re-apply backend patches, but discard parts that overlap.
+
-            
+                # 2. Apply frontend patch first to the state at client_base_seq
-            frontend_paths = {p['path'] for p in patch}
+                # But we only have the current authoritative model.
-            
+                # "Apply the frontend patch first to the model (frontend wins)"
-            current_state = entry.model.model_dump(mode="json")
+                # "Re-apply the backend deltas that do not overlap the frontend's changed paths on top"
-            # Apply frontend patch
+
-            new_state = jsonpatch.apply_patch(current_state, patch)
+                # Let's get the state as it was at client_base_seq if possible?
-            
+                # No, history only has patches.
-            # Re-apply backend patches that don't overlap
+
-            for b_patch in backend_patches:
+                # Alternative: Apply frontend patch to current model.
-                filtered_b_patch = [op for op in b_patch if op['path'] not in frontend_paths]
+                # Then re-apply backend patches, but discard parts that overlap.
-                if filtered_b_patch:
+
-                    new_state = jsonpatch.apply_patch(new_state, filtered_b_patch)
+                frontend_paths = {p['path'] for p in patch}
-            
+
-            self._update_model(entry.model, new_state)
+                current_state = entry.model.model_dump(mode="json")
-            
+                # Apply frontend patch
-            # Commit the result as a single new patch
+                try:
-            # We need to compute what changed from last_snapshot to new_state
+                    new_state = jsonpatch.apply_patch(current_state, patch)
-            final_patch = jsonpatch.make_patch(entry.last_snapshot, new_state).patch
+                except jsonpatch.JsonPatchConflict as e:
-            if final_patch:
+                    logger.warning(f"apply_client_patch: Failed to apply client patch during conflict resolution: {e}. Sending snapshot to resync.")
-                entry.commit_patch(final_patch)
+                    # Send snapshot to force resync
-                # Broadcast resolved state as snapshot to converge
+                    if self.websocket:
-                if self.websocket:
+                        msg = SnapshotMessage(
-                    msg = SnapshotMessage(
+                            store=entry.store_name,
-                        store=entry.store_name,
+                            seq=entry.seq,
-                        seq=entry.seq,
+                            state=entry.model.model_dump(mode="json")
-                        state=entry.model.model_dump(mode="json")
+                        )
-                    )
+                        await self.websocket.send_json(msg.model_dump(mode="json"))
-                    await self.websocket.send_json(msg.model_dump(mode="json"))
+                    return
                # Re-apply backend patches that don't overlap
                for b_patch in backend_patches:
                    filtered_b_patch = [op for op in b_patch if op['path'] not in frontend_paths]
                    if filtered_b_patch:
                        try:
                            new_state = jsonpatch.apply_patch(new_state, filtered_b_patch)
                        except jsonpatch.JsonPatchConflict as e:
                            logger.warning(f"apply_client_patch: Failed to apply backend patch during conflict resolution: {e}. Skipping this patch.")
                            continue
                self._update_model(entry.model, new_state)
                # Commit the result as a single new patch
                # We need to compute what changed from last_snapshot to new_state
                final_patch = jsonpatch.make_patch(entry.last_snapshot, new_state).patch
                if final_patch:
                    entry.commit_patch(final_patch)
                    # Broadcast resolved state as snapshot to converge
                    if self.websocket:
                        msg = SnapshotMessage(
                            store=entry.store_name,
                            seq=entry.seq,
                            state=entry.model.model_dump(mode="json")
                        )
                        await self.websocket.send_json(msg.model_dump(mode="json"))
        except Exception as e:
            logger.error(f"apply_client_patch: Unexpected error: {e}. Sending snapshot to resync.", exc_info=True)
            # Send snapshot to force resync
            if self.websocket:
                msg = SnapshotMessage(
                    store=entry.store_name,
                    seq=entry.seq,
                    state=entry.model.model_dump(mode="json")
                )
                await self.websocket.send_json(msg.model_dump(mode="json"))
    def _update_model(self, model: BaseModel, new_data: Dict[str, Any]):
        # Update model using model_validate for potentially nested models
--- a/web/src/App.vue
+++ b/web/src/App.vue
@@ -7,6 +7,7 @@ import ChatPanel from './components/ChatPanel.vue'
 import LoginScreen from './components/LoginScreen.vue'
 import { useOrderStore } from './stores/orders'
 import { useChartStore } from './stores/chart'
 import { useShapeStore } from './stores/shapes'
 import { useStateSync } from './composables/useStateSync'
 import { wsManager } from './composables/useWebSocket'
@@ -63,9 +64,11 @@ const handleAuthenticate = async (
      // Initialize state sync after successful authentication
      const orderStore = useOrderStore()
      const chartStore = useChartStore()
      const shapeStore = useShapeStore()
      const stateSync = useStateSync({
        OrderStore: orderStore,
-        ChartStore: chartStore
+        ChartStore: chartStore,
        ShapeStore: shapeStore
      })
      stateSyncCleanup = stateSync.cleanup
    } else {
--- a/web/src/components/ChartView.vue
+++ b/web/src/components/ChartView.vue
@@ -2,6 +2,7 @@
 import { ref, onMounted, onBeforeUnmount, watch } from 'vue'
 import Card from 'primevue/card'
 import { createTradingViewDatafeed } from '../composables/useTradingViewDatafeed'
 import { useTradingViewShapes } from '../composables/useTradingViewShapes'
 import { useChartStore } from '../stores/chart'
 import type { IChartingLibraryWidget } from '../types/tradingview'
@@ -10,6 +11,7 @@ const chartStore = useChartStore()
 let tvWidget: IChartingLibraryWidget | null = null
 let datafeed: any = null
 let isUpdatingFromChart = false // Flag to prevent circular updates
 let shapeCleanup: (() => void) | null = null // Cleanup function for shape sync
 onMounted(() => {
  if (!chartContainer.value) return
@@ -49,6 +51,10 @@ onMounted(() => {
        setupStoreWatchers()
        // Initialize visible range on chart load
        initializeVisibleRange()
        // Setup shape synchronization
        if (tvWidget) {
          shapeCleanup = useTradingViewShapes(tvWidget)
        }
      })
    } catch (error) {
      console.error('Failed to initialize TradingView widget:', error)
@@ -165,6 +171,12 @@ function setupStoreWatchers() {
 }
 onBeforeUnmount(() => {
  // Cleanup shape synchronization
  if (shapeCleanup) {
    shapeCleanup()
    shapeCleanup = null
  }
  if (tvWidget) {
    tvWidget.remove()
    tvWidget = null
--- a/web/src/components/ChatPanel.vue
+++ b/web/src/components/ChatPanel.vue
@@ -271,9 +271,9 @@ const chatStyles = computed(() => JSON.stringify({
  },
  message: {
    background: '#161e35',
-    backgroundMe: '#00d4aa',
+    backgroundMe: '#1e2d4f',
    color: '#cdd6e8',
-    colorMe: '#0a0e1a'
+    colorMe: '#cdd6e8'
  }
 }))
--- a/web/src/composables/useTradingViewShapes.ts
+++ b/web/src/composables/useTradingViewShapes.ts
@@ -0,0 +1,735 @@
 import { watch } from 'vue'
 import type { IChartingLibraryWidget } from '../types/tradingview'
 import { useShapeStore } from '../stores/shapes'
 import { useChartStore } from '../stores/chart'
 import type { Shape, ControlPoint } from '../stores/shapes'
 /**
 * Map TradingView internal tool names (LineToolXXX) to TradingView's native shape type names
 * Used when converting user-drawn shapes to our Shape format
 */
 const TV_TOOL_TO_SHAPE_TYPE: Record<string, string> = {
  'LineToolTrendLine': 'trend_line',
  'LineToolHorzLine': 'horizontal_line',
  'LineToolVertLine': 'vertical_line',
  'LineToolRectangle': 'rectangle',
  'LineToolFibRetracement': 'fib_retracement',
  'LineToolFibExtension': 'fib_trend_ext',
  'LineToolParallelChannel': 'parallel_channel',
  'LineToolArrow': 'arrow',
  'LineToolText': 'text',
  'LineToolCallout': 'text',
  'LineToolCircle': 'circle',
  'LineToolPath': 'path',
  'LineToolPitchfork': 'pitchfork',
  'LineToolGannFan': 'gannbox_fan',
  'LineToolHeadAndShoulders': 'head_and_shoulders'
 }
 /**
 * Map line styles between our format and TradingView format
 */
 const LINE_STYLE_TO_TV: Record<string, number> = {
  'solid': 0,
  'dashed': 1,
  'dotted': 2
 }
 const TV_TO_LINE_STYLE: Record<number, string> = {
  0: 'solid',
  1: 'dashed',
  2: 'dotted'
 }
 /**
 * Convert TradingView shape to our Shape format
 */
 function convertTVShapeToShape(tvShape: any, symbol: string, shapeId?: string, shapeName?: string): Shape {
  // Get shape ID - try multiple ways
  let id = shapeId || tvShape.id
  if (!id && typeof tvShape.getId === 'function') {
    try {
      id = tvShape.getId()
    } catch (e) {
      console.warn('[TradingView Shapes] Error calling getId:', e)
    }
  }
  // Get shape name/type - use provided name first, then try multiple properties
  if (!shapeName) {
    shapeName = tvShape.name
  }
  if (!shapeName && typeof tvShape.getName === 'function') {
    try {
      shapeName = tvShape.getName()
    } catch (e) {
      console.warn('[TradingView Shapes] Error calling getName:', e)
    }
  }
  // Fallback to checking shape type or tool name
  if (!shapeName) {
    shapeName = tvShape.toolName || tvShape.type || 'unknown'
  }
  const standardType = TV_TOOL_TO_SHAPE_TYPE[shapeName] || shapeName
  // Extract control points from TradingView shape
  const points: ControlPoint[] = []
  // Try multiple ways to get points from the shape
  let pointsData = tvShape.points || []
  // If getPoints method exists, use it
  if (typeof tvShape.getPoints === 'function') {
    try {
      pointsData = tvShape.getPoints() || []
    } catch (e) {
      console.warn('[TradingView Shapes] Error calling getPoints:', e)
    }
  }
  // Convert points to our format
  if (Array.isArray(pointsData)) {
    for (const point of pointsData) {
      if (point && typeof point === 'object') {
        const controlPoint: ControlPoint = {
          time: Math.floor(point.time),
          price: point.price
        }
        // Only include channel if it's actually set
        if (point.channel !== undefined && point.channel !== null) {
          controlPoint.channel = point.channel
        }
        points.push(controlPoint)
      }
    }
  }
  console.log(`[TradingView Shapes] Converting shape ${id} (${shapeName}), found ${points.length} points`, points)
  // Extract properties
  let properties = tvShape.properties || {}
  // If getProperties method exists, use it
  if (typeof tvShape.getProperties === 'function') {
    try {
      properties = tvShape.getProperties() || properties
    } catch (e) {
      console.warn('[TradingView Shapes] Error calling getProperties:', e)
    }
  }
  const color = properties.linecolor || '#2962FF'
  const lineWidth = properties.linewidth || 1
  const lineStyle = TV_TO_LINE_STYLE[properties.linestyle] || 'solid'
  const now = Math.floor(Date.now() / 1000)
  return {
    id,
    type: standardType,
    points,
    color,
    line_width: lineWidth,
    line_style: lineStyle,
    properties: {
      ...properties,
      // Store original TradingView shape name for reference
      tv_shape_name: shapeName
    },
    symbol,
    created_at: now,
    modified_at: now
  }
 }
 /**
 * Convert interval string to seconds
 */
 function intervalToSeconds(interval: string): number {
  // Handle plain numbers (TradingView uses integers for sub-hour intervals in minutes)
  const numericInterval = parseInt(interval)
  if (!isNaN(numericInterval) && interval === numericInterval.toString()) {
    return numericInterval * 60 // Plain number means minutes
  }
  // Handle formatted intervals like "15M", "1H", "1D"
  const match = interval.match(/^(\d+)([SMHDW])$/)
  if (!match) {
    console.warn('[TradingView Shapes] Unknown interval format:', interval)
    return 60 // Default to 1 minute
  }
  const value = parseInt(match[1])
  const unit = match[2]
  const multipliers: Record<string, number> = {
    'S': 1,
    'M': 60,
    'H': 3600,
    'D': 86400,
    'W': 604800
  }
  return value * (multipliers[unit] || 60)
 }
 /**
 * Canonicalize timestamp to candle boundary
 * TradingView requires timestamps to align exactly with candle start times
 */
 function canonicalizeTimestamp(timestamp: number, intervalSeconds: number): number {
  // Round down to nearest candle boundary
  return Math.floor(timestamp / intervalSeconds) * intervalSeconds
 }
 /**
 * Setup bidirectional synchronization between TradingView shapes and ShapeStore
 */
 export function useTradingViewShapes(tvWidget: IChartingLibraryWidget) {
  const shapeStore = useShapeStore()
  const chartStore = useChartStore()
  let isApplyingTVUpdate = false // Prevent circular updates when TV modifies shapes
  let isUpdatingStore = false // Prevent circular updates when we update the store from TV
  let isChartReady = false // Track if chart API is ready
  /**
   * Handle drawing events from TradingView
   */
  function handleDrawingEvent(shapeId: string, eventType: string) {
    if (isApplyingTVUpdate) return
    console.log('[TradingView Shapes] Drawing event:', eventType, shapeId)
    try {
      const chart = tvWidget.activeChart()
      if (!chart) return
      const currentSymbol = chartStore.chart_state.symbol
      if (eventType === 'remove') {
        isUpdatingStore = true
        shapeStore.removeShape(shapeId)
        isUpdatingStore = false
        return
      }
      // First get the shape name from getAllShapes (which has id and name)
      let shapeName: string | undefined
      const allShapes = chart.getAllShapes()
      if (allShapes) {
        const shapeInfo = allShapes.find((s: any) => s.id === shapeId)
        if (shapeInfo) {
          shapeName = shapeInfo.name
        }
      }
      // Then get the actual shape entity using getShapeById (which has methods)
      let tvShape
      try {
        tvShape = chart.getShapeById(shapeId)
      } catch (error) {
        console.warn('[TradingView Shapes] Could not get shape:', shapeId)
        return
      }
      if (!tvShape) return
      // Debug: log on first encounter
      if (eventType === 'create') {
        console.log('[TradingView Shapes] Shape entity methods:', Object.keys(tvShape))
        console.log('[TradingView Shapes] Shape name from getAllShapes:', shapeName)
      }
      if (eventType === 'create') {
        // Get points immediately - don't create shape without them
        const points = tvShape.getPoints()
        const controlPoints = points && points.length > 0 ? points.map(p => {
          const cp: ControlPoint = {
            time: Math.floor(p.time),
            price: p.price
          }
          if (p.channel !== undefined && p.channel !== null) {
            cp.channel = p.channel
          }
          return cp
        }) : []
        // Create shape with complete data
        const shape = convertTVShapeToShape(tvShape, currentSymbol, shapeId, shapeName)
        // Override points with the ones we just fetched
        shape.points = controlPoints
        console.log('[TradingView Shapes] Creating shape:', shapeId, shape.type, 'with', shape.points.length, 'points')
        isUpdatingStore = true
        shapeStore.addShape(shape)
        isUpdatingStore = false
      } else if (eventType === 'points_changed') {
        // Points updated - get latest points and create NEW array
        const points = tvShape.getPoints()
        if (points) {
          // Create a completely new array to ensure it's treated as a replacement
          const controlPoints = Array.from(points.map(p => {
            const cp: ControlPoint = {
              time: Math.floor(p.time),
              price: p.price
            }
            if (p.channel !== undefined && p.channel !== null) {
              cp.channel = p.channel
            }
            return cp
          }))
          console.log('[TradingView Shapes] Points changed:', shapeId, 'now has', controlPoints.length, 'points')
          isUpdatingStore = true
          shapeStore.updateShape(shapeId, { points: controlPoints })
          isUpdatingStore = false
        }
      } else if (eventType === 'properties_changed') {
        // Properties updated - get latest properties
        const properties = tvShape.getProperties()
        if (properties) {
          const color = properties.linecolor || '#2962FF'
          const lineWidth = properties.linewidth || 1
          const lineStyle = TV_TO_LINE_STYLE[properties.linestyle] || 'solid'
          console.log('[TradingView Shapes] Properties changed:', shapeId)
          isUpdatingStore = true
          shapeStore.updateShape(shapeId, {
            color,
            line_width: lineWidth,
            line_style: lineStyle,
            properties
          })
          isUpdatingStore = false
        }
      } else if (eventType === 'move') {
        // Shape moved - update points with NEW array
        const points = tvShape.getPoints()
        if (points) {
          const controlPoints = Array.from(points.map(p => {
            const cp: ControlPoint = {
              time: Math.floor(p.time),
              price: p.price
            }
            if (p.channel !== undefined && p.channel !== null) {
              cp.channel = p.channel
            }
            return cp
          }))
          console.log('[TradingView Shapes] Shape moved:', shapeId)
          isUpdatingStore = true
          shapeStore.updateShape(shapeId, { points: controlPoints })
          isUpdatingStore = false
        }
      }
    } catch (error) {
      console.error('[TradingView Shapes] Error handling drawing event:', error)
    }
  }
  /**
   * Setup listeners for TradingView shape events
   */
  function setupShapeListeners() {
    try {
      const chart = tvWidget.activeChart()
      if (!chart) {
        console.warn('[TradingView Shapes] Active chart not available yet')
        return () => {}
      }
      isChartReady = true
      console.log('[TradingView Shapes] Setting up drawing event subscriptions')
      // Subscribe to the drawing event which handles all shape events
      const unsubscribe = tvWidget.subscribe('drawing_event', (shapeId: string, eventType: string) => {
        handleDrawingEvent(shapeId, eventType)
      })
      // Initial sync on data load
      const dataLoadedSubscription = chart.onDataLoaded().subscribe(null, () => {
        console.log('[TradingView Shapes] Chart data loaded, performing initial sync')
        syncShapesFromTV()
      })
      // Cleanup function to unsubscribe
      return () => {
        console.log('[TradingView Shapes] Cleaning up event subscriptions')
        try {
          if (typeof unsubscribe === 'function') {
            unsubscribe()
          } else {
            console.warn('[TradingView Shapes] unsubscribe is not a function:', typeof unsubscribe)
          }
          if (dataLoadedSubscription && typeof dataLoadedSubscription.unsubscribe === 'function') {
            dataLoadedSubscription.unsubscribe()
          }
        } catch (error) {
          console.error('[TradingView Shapes] Error during cleanup:', error)
        }
      }
    } catch (error) {
      console.error('[TradingView Shapes] Error setting up shape listeners:', error)
      return () => {}
    }
  }
  /**
   * Scan TradingView for shapes and sync to store
   */
  function syncShapesFromTV() {
    if (!isChartReady) {
      return // Skip if chart isn't ready yet
    }
    try {
      const chart = tvWidget.activeChart()
      if (!chart) {
        return // Chart not available
      }
      const allShapes = chart.getAllShapes()
      if (!allShapes) return
      const currentSymbol = chartStore.chart_state.symbol
      // Track which shape IDs we've seen
      const seenIds = new Set<string>()
      isUpdatingStore = true
      try {
        for (const tvShape of allShapes) {
          seenIds.add(tvShape.id)
          const existingShape = shapeStore.getShape(tvShape.id)
          const newShape = convertTVShapeToShape(tvShape, currentSymbol)
          if (!existingShape) {
            // New shape - add it
            console.log('[TradingView Shapes] New shape detected:', tvShape.id, tvShape.name, 'with', newShape.points.length, 'points')
            shapeStore.addShape(newShape)
          } else {
            // Check if shape has changed
            const pointsChanged = JSON.stringify(existingShape.points) !== JSON.stringify(newShape.points)
            const hasChanged =
              pointsChanged ||
              existingShape.color !== newShape.color ||
              existingShape.line_width !== newShape.line_width ||
              existingShape.line_style !== newShape.line_style
            if (hasChanged) {
              if (pointsChanged) {
                console.log('[TradingView Shapes] Shape points changed:', tvShape.id,
                  'from', existingShape.points.length, 'to', newShape.points.length, 'points')
              } else {
                console.log('[TradingView Shapes] Shape properties modified:', tvShape.id)
              }
              shapeStore.updateShape(tvShape.id, {
                points: newShape.points,
                color: newShape.color,
                line_width: newShape.line_width,
                line_style: newShape.line_style,
                properties: newShape.properties
              })
            }
          }
        }
        // Check for deleted shapes
        const allStoreShapes = shapeStore.getAllShapes()
        for (const storeShape of allStoreShapes) {
          if (storeShape.symbol === currentSymbol && !seenIds.has(storeShape.id)) {
            console.log('[TradingView Shapes] Shape deleted:', storeShape.id)
            shapeStore.removeShape(storeShape.id)
          }
        }
      } finally {
        isUpdatingStore = false
      }
    } catch (error) {
      console.error('[TradingView Shapes] Error syncing shapes from TV:', error)
    }
  }
  /**
   * Setup watchers for ShapeStore changes to apply to TradingView
   */
  function setupStoreWatchers() {
    // Watch for shape store changes and apply to TradingView
    watch(
      () => shapeStore.shapes,
      async (newShapes, oldShapes) => {
        if (isUpdatingStore || !isChartReady) return
        isApplyingTVUpdate = true
        try {
          const chart = tvWidget.activeChart()
          if (!chart) return
          const currentSymbol = chartStore.chart_state.symbol
          // Find added shapes
          for (const [id, shape] of Object.entries(newShapes)) {
            // Only apply shapes for current symbol (or shapes with null symbol)
            if (shape.symbol && shape.symbol !== currentSymbol) continue
            if (!oldShapes || !oldShapes[id]) {
              // Shape was added to store
              await createTVShape(shape)
            } else {
              // Check if shape exists in TradingView
              let tvShape = null
              try {
                tvShape = chart.getShapeById(id)
              } catch (e) {
                // Shape doesn't exist in TV
              }
              if (!tvShape) {
                // Shape is in store but not in TradingView - create it
                await createTVShape(shape)
              } else if (JSON.stringify(shape) !== JSON.stringify(oldShapes[id])) {
                // Shape was modified
                updateTVShape(shape)
              }
            }
          }
          // Find removed shapes
          if (oldShapes) {
            for (const [id, shape] of Object.entries(oldShapes)) {
              if (shape.symbol && shape.symbol !== currentSymbol) continue
              if (!newShapes[id]) {
                removeTVShape(id)
              }
            }
          }
        } catch (error) {
          console.error('[TradingView Shapes] Error applying store changes:', error)
        } finally {
          isApplyingTVUpdate = false
        }
      },
      { deep: true }
    )
  }
  /**
   * Create a shape in TradingView
   */
  async function createTVShape(shape: Shape) {
    if (!isChartReady) return
    try {
      const chart = tvWidget.activeChart()
      if (!chart) return
      const currentSymbol = chartStore.chart_state.symbol
      // Get current chart interval and convert to seconds for timestamp canonicalization
      const interval = chartStore.chart_state.interval
      const intervalSeconds = intervalToSeconds(interval)
      // Convert points to TradingView format and canonicalize timestamps to candle boundaries
      const tvPoints = shape.points.map(p => {
        const canonicalTime = canonicalizeTimestamp(p.time, intervalSeconds)
        const point: any = {
          time: canonicalTime,
          price: p.price
        }
        // Only include channel if it has a value (for parallel channels, etc.)
        if (p.channel != null) {
          point.channel = p.channel
        }
        return point
      })
      // Build overrides - merge color/width/style with properties
      const overrides: any = {
        ...shape.properties
      }
      if (shape.color) {
        overrides.linecolor = shape.color
      }
      if (shape.line_width) {
        overrides.linewidth = shape.line_width
      }
      if (shape.line_style) {
        overrides.linestyle = LINE_STYLE_TO_TV[shape.line_style] || 0
      }
      const options = {
        shape: shape.type,
        zOrder: 'top',
        lock: false,
        disableSelection: false,
        disableSave: false,
        disableUndo: false,
        overrides
      }
      console.log(`[TradingView Shapes] Creating shape type="${shape.type}" with ${tvPoints.length} points:`, tvPoints)
      // Create the shape - use createShape for single points, createMultipointShape for multiple
      let entityIdPromise
      if (tvPoints.length === 1) {
        console.log(`[TradingView Shapes] Using createShape() for single point`)
        entityIdPromise = chart.createShape(tvPoints[0], options)
      } else {
        console.log(`[TradingView Shapes] Using createMultipointShape() for ${tvPoints.length} points`)
        entityIdPromise = chart.createMultipointShape(tvPoints, options)
      }
      const entityId = await entityIdPromise
      // If TradingView assigned a different ID, update the store
      if (entityId && entityId !== shape.id) {
        isUpdatingStore = true
        await new Promise(resolve => setTimeout(resolve, 0))
        try {
          shapeStore.removeShape(shape.id)
          const updatedShape: Shape = {
            ...shape,
            id: entityId,
            original_id: shape.original_id || shape.id,
            symbol: shape.symbol || currentSymbol
          }
          shapeStore.addShape(updatedShape)
        } finally {
          await new Promise(resolve => setTimeout(resolve, 0))
          isUpdatingStore = false
        }
      }
      // Bring shape to front to ensure visibility
      try {
        const createdShape = chart.getShapeById(entityId)
        if (createdShape && typeof createdShape.bringToFront === 'function') {
          createdShape.bringToFront()
        }
      } catch (e) {
        // Ignore errors
      }
    } catch (error) {
      console.error('[TradingView Shapes] Error creating shape:', error)
    }
  }
  /**
   * Update a shape in TradingView
   */
  function updateTVShape(shape: Shape) {
    if (!isChartReady) return
    try {
      const chart = tvWidget.activeChart()
      if (!chart) return
      const tvShape = chart.getShapeById(shape.id)
      if (!tvShape) {
        console.warn('[TradingView Shapes] Shape not found in TV, creating instead:', shape.id)
        createTVShape(shape)
        return
      }
      // Update points
      const tvPoints = shape.points.map(p => {
        const point: { time: number; price: number; channel?: string } = {
          time: p.time,
          price: p.price
        }
        if (p.channel) {
          point.channel = p.channel
        }
        return point
      })
      tvShape.setPoints(tvPoints)
      // Update properties
      tvShape.setProperties({
        linecolor: shape.color || '#2962FF',
        linewidth: shape.line_width || 1,
        linestyle: LINE_STYLE_TO_TV[shape.line_style || 'solid'] || 0,
        ...shape.properties
      })
      console.log('[TradingView Shapes] Updated shape in TV:', shape.id)
    } catch (error) {
      console.error('[TradingView Shapes] Error updating shape in TV:', error)
    }
  }
  /**
   * Remove a shape from TradingView
   */
  function removeTVShape(shapeId: string) {
    if (!isChartReady) return
    try {
      const chart = tvWidget.activeChart()
      if (!chart) return
      const tvShape = chart.getShapeById(shapeId)
      if (tvShape) {
        chart.removeEntity(tvShape)
        console.log('[TradingView Shapes] Removed shape from TV:', shapeId)
      }
    } catch (error) {
      console.error('[TradingView Shapes] Error removing shape from TV:', error)
    }
  }
  /**
   * Setup selection tracking
   */
  function setupSelectionTracking() {
    if (!isChartReady) return
    try {
      const chart = tvWidget.activeChart()
      if (!chart) return
      chart.onSelectionChanged().subscribe(null, () => {
        const selectedObjects = chart.selection()
        // Filter for shape/drawing objects only
        const selectedShapeIds = selectedObjects
          .filter((obj: any) => obj.type === 'study' || obj.name?.startsWith('LineTool'))
          .map((obj: any) => obj.id)
        console.log('[TradingView Shapes] Selection changed:', selectedShapeIds)
        chartStore.chart_state.selected_shapes = selectedShapeIds
      })
    } catch (error) {
      console.warn('[TradingView Shapes] Selection tracking not available:', error)
    }
  }
  // Initialize
  const cleanupShapeListeners = setupShapeListeners()
  setupStoreWatchers()
  setupSelectionTracking()
  // Return cleanup function
  return () => {
    cleanupShapeListeners()
  }
 }
--- a/web/src/stores/chart.ts
+++ b/web/src/stores/chart.ts
@@ -6,6 +6,7 @@ export interface ChartState {
  start_time: number | null
  end_time: number | null
  interval: string
  selected_shapes: string[]
 }
 export const useChartStore = defineStore('ChartStore', () => {
@@ -13,7 +14,8 @@ export const useChartStore = defineStore('ChartStore', () => {
    symbol: 'BINANCE:BTC/USDT',
    start_time: null,
    end_time: null,
-    interval: '15'
+    interval: '15',
    selected_shapes: []
  })
  return { chart_state }
--- a/web/src/stores/shapes.ts
+++ b/web/src/stores/shapes.ts
@@ -0,0 +1,64 @@
 import { defineStore } from 'pinia'
 import { ref } from 'vue'
 export interface ControlPoint {
  time: number
  price: number
  channel?: string
 }
 export interface Shape {
  id: string
  type: string
  points: ControlPoint[]
  color?: string
  line_width?: number
  line_style?: string
  properties?: Record<string, any>
  symbol?: string
  created_at?: number
  modified_at?: number
  original_id?: string  // Original ID from backend/agent before TradingView assigns its own ID
 }
 export const useShapeStore = defineStore('ShapeStore', () => {
  const shapes = ref<Record<string, Shape>>({})
  // Helper methods
  const addShape = (shape: Shape) => {
    shapes.value[shape.id] = shape
  }
  const updateShape = (id: string, updates: Partial<Shape>) => {
    if (shapes.value[id]) {
      const updated = {
        ...shapes.value[id],
        ...updates,
        modified_at: Math.floor(Date.now() / 1000)
      }
      // Replace the entire shape object to ensure arrays are replaced atomically
      shapes.value[id] = updated
    }
  }
  const removeShape = (id: string) => {
    delete shapes.value[id]
  }
  const getShape = (id: string): Shape | undefined => {
    return shapes.value[id]
  }
  const getAllShapes = (): Shape[] => {
    return Object.values(shapes.value)
  }
  return {
    shapes,
    addShape,
    updateShape,
    removeShape,
    getShape,
    getAllShapes
  }
 })