Add account data fetching and display balances in dashboard

Add gold_silver_ratio indicator (xyz:GOLD / xyz:SILVER) with fallback
reference of 61.59, mirroring the existing WTI/BRENT ratio setup.
Also register xyz:GOLD and xyz:SILVER in WATCHED_COINS and data_fetcher
defaults so the candle data is fetched for the new indicator.

WIKI/dashboard_configuration.md

@@ -0,0 +1,90 @@
+# Dashboard Configuration Guide
+
+This guide explains how to configure which tables are displayed on the live terminal dashboard.
+
+## Overview
+
+The dashboard is rendered by the `DashboardRenderer` class in `dashboard.py`. It currently supports two tables:
+
+| Table Key | Title | Description |
+|-----------|-------|-------------|
+| `market` | Market Dashboard | Live prices, best bid/ask, gap, and direction for watched coins |
+| `strategies` | Strategies | Signal, signal price, last change, timeframe, and size for each enabled strategy |
+
+Each table can be independently enabled or disabled. When only one table is visible, it takes the full terminal width. When both are visible, they split side-by-side.
+
+## Configuration
+
+### Default Visibility
+
+The default table visibility is set when `DashboardRenderer` is instantiated in `main_app.py` (`MainApp.__init__`):
+
+```python
+self.renderer = DashboardRenderer(table_visibility={
+    "market": True,
+    "strategies": False,
+})
+```
+
+By default, the **market table is enabled** and the **strategies table is disabled**.
+
+### Changing Default Visibility
+
+To change which tables are shown by default, edit the `table_visibility` dict in `main_app.py` (`MainApp.__init__`, line 349):
+
+```python
+self.renderer = DashboardRenderer(table_visibility={
+    "market": True,
+    "strategies": True,   # enable strategies table
+})
+```
+
+### Runtime Toggling
+
+Tables can be toggled at runtime through the `MainApp.toggle_table()` method, which delegates to `DashboardRenderer.toggle_table()`:
+
+```python
+# Flip the strategies table on/off
+app.toggle_table("strategies")
+
+# Explicitly enable
+app.toggle_table("strategies", enabled=True)
+
+# Explicitly disable
+app.toggle_table("strategies", enabled=False)
+```
+
+The same methods are available directly on the renderer:
+
+```python
+renderer = DashboardRenderer()
+renderer.toggle_table("market")              # flip
+renderer.toggle_table("strategies", False)   # disable
+```
+
+## How It Works
+
+### DashboardRenderer (`dashboard.py`)
+
+- `__init__(console=None, table_visibility=None)` — accepts an optional `table_visibility` dict. If not provided, defaults to `{"market": True, "strategies": False}`.
+- `toggle_table(table_name, enabled=None)` — flips the visibility state when `enabled` is `None`, or sets it to the given boolean. Raises `ValueError` for unknown table names.
+- `build_layout(...)` — conditionally builds only the tables that are enabled, then arranges them:
+  - **One table:** `Layout(table)` — full width
+  - **Two tables:** `Layout.split_row(Layout(t1), Layout(t2))` — side-by-side
+  - **Zero tables:** empty `Layout`
+
+### MainApp (`main_app.py`)
+
+- `MainApp.__init__` creates the `DashboardRenderer` with the `table_visibility` config.
+- `MainApp.toggle_table(table_name, enabled=None)` delegates to the renderer for runtime toggling.
+- `MainApp.display_dashboard()` calls `renderer.build_layout()` which respects the current visibility settings.
+
+## File Reference
+
+| File | Line | Description |
+|------|------|-------------|
+| `dashboard.py` | 22 | `DashboardRenderer.__init__` — accepts `table_visibility` parameter |
+| `dashboard.py` | 32 | `toggle_table()` method — flips or sets table visibility |
+| `dashboard.py` | 172 | `build_layout()` — conditionally includes tables based on visibility |
+| `main_app.py` | 349 | `MainApp.__init__` — sets default `table_visibility` |
+| `main_app.py` | 385 | `MainApp.toggle_table()` — runtime toggle method |

WIKI/indicators.md

@@ -0,0 +1,263 @@
+# Indicators Guide
+
+This guide explains how to configure and use the Indicators table on the live terminal dashboard.
+
+## Overview
+
+The Indicators table displays computed financial indicators (e.g., WTI/BRENT ratio, live prices, moving averages, RSI) with their current value, 1-hour and 1-day percentage changes, and deviation from a long-term average.
+
+The system is **config-driven** — new indicators are added by editing `_data/indicators.json`. No code changes are required for standard indicator types.
+
+## Dashboard Table
+
+The Indicators table is displayed below the Market table in the dashboard. It shows:
+
+| Column | Description |
+|--------|-------------|
+| `#` | Indicator number |
+| `Indicator` | Display name from config |
+| `Value` | Current indicator value |
+| `1h Change` | Percentage change over the last 1 hour |
+| `1D Change` | Percentage change over the last 1 day |
+| `Deviation` | Deviation from the long-term average |
+
+Changes are color-coded: **green** for positive, **red** for negative, **yellow** for neutral.
+
+## Configuration
+
+### Default Visibility
+
+The Indicators table is enabled by default. The visibility is set in `main_app.py` (`MainApp.__init__`):
+
+```python
+self.renderer = DashboardRenderer(table_visibility={
+    "market": True,
+    "strategies": False,
+    "indicators": True,
+})
+```
+
+### Runtime Toggling
+
+Toggle the Indicators table at runtime:
+
+```python
+app.toggle_table("indicators")              # flip on/off
+app.toggle_table("indicators", enabled=True)  # explicitly enable
+app.toggle_table("indicators", enabled=False) # explicitly disable
+```
+
+## Indicator Types
+
+The following indicator types are supported in `_data/indicators.json`:
+
+### `ratio` — A/B Ratio
+
+Computes `numerator / denominator`.
+
+```json
+"wti_brent_ratio": {
+    "display_name": "WTI/BRENT",
+    "type": "ratio",
+    "numerator": "xyz:CL",
+    "denominator": "xyz:BRENTOIL",
+    "changes": ["1h", "1d"],
+    "show_deviation": true,
+    "min_data_points": 100,
+    "fallback_reference": 0.96065
+}
+```
+
+- **Value**: `live(numerator) / live(denominator)` from latest 1m candle closes
+- **1h Change**: compares to ratio from 1h candle close prices
+- **1D Change**: compares to ratio from 1d candle close prices
+- **Deviation**: `(current - long_avg) / long_avg * 100`, where `long_avg` is the mean of daily ratios over all available history. If fewer than `min_data_points` (default 100) daily data points exist and `fallback_reference` is set, the fallback value is used instead.
+
+```json
+"gold_silver_ratio": {
+    "display_name": "GOLD/SILVER",
+    "type": "ratio",
+    "numerator": "xyz:GOLD",
+    "denominator": "xyz:SILVER",
+    "changes": ["1h", "1d"],
+    "show_deviation": true,
+    "min_data_points": 100,
+    "fallback_reference": 61.59
+}
+```
+
+### `price` — Single Price
+
+```json
+"wti_price": {
+    "display_name": "WTI",
+    "type": "price",
+    "coin": "xyz:CL",
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+- **Value**: latest close price from `{coin}_1m` table
+- **1h/1D Change**: compares to close from 1h/1d candle tables
+- **Deviation**: `(current - long_avg) / long_avg * 100`, where `long_avg` is the mean of daily closes
+
+### `spread` — Price Difference
+
+Computes `numerator - denominator`.
+
+```json
+"wti_brent_spread": {
+    "display_name": "WTI-BRENT Spread",
+    "type": "spread",
+    "numerator": "xyz:CL",
+    "denominator": "xyz:BRENTOIL",
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+### `diff_pct` — Percentage Difference
+
+Computes `(numerator - denominator) / denominator * 100`.
+
+```json
+"wti_brent_diff": {
+    "display_name": "WTI-BRENT Diff%",
+    "type": "diff_pct",
+    "numerator": "xyz:CL",
+    "denominator": "xyz:BRENTOIL",
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+### `ma` — Moving Average
+
+```json
+"wti_ma_20": {
+    "display_name": "WTI MA(20)",
+    "type": "ma",
+    "coin": "xyz:CL",
+    "timeframe": "1h",
+    "period": 20,
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+- **Value**: latest SMA value on the specified timeframe
+- **1h Change**: compares to MA value from 1h candle table
+- **1D Change**: compares to MA value from 1d candle table
+- **Deviation**: `(current_price - MA) / MA * 100` (how far the live price is from the MA)
+
+### `rsi` — Relative Strength Index
+
+```json
+"wti_rsi_14": {
+    "display_name": "WTI RSI(14)",
+    "type": "rsi",
+    "coin": "xyz:CL",
+    "timeframe": "1h",
+    "period": 14,
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+- **Value**: latest RSI value (0-100) on the specified timeframe
+- **1h/1D Change**: absolute change in RSI points
+- **Deviation**: `RSI - 50` (deviation from neutral)
+
+### `custom` — Custom Function
+
+Calls a user-defined Python function.
+
+```json
+"custom_indicator": {
+    "display_name": "My Custom Indicator",
+    "type": "custom",
+    "module": "indicators.custom_indicators",
+    "function": "my_custom_calc",
+    "args": {"param1": "value1"},
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+The custom function must accept `db_path` as the first argument, plus any `args` from the config, and return a dict:
+
+```python
+def my_custom_calc(db_path, **kwargs):
+    return {
+        "value": 0.96611,
+        "reference": 0.96044,
+        "changes": {"1h": 0.12, "1d": -0.45},
+        "deviation": 0.59
+    }
+}
+```
+
+## Data Sources
+
+All indicator calculations read from the SQLite database `_data/market_data.db`:
+
+- **Live value**: latest close price from `{coin}_1m` candle table (updated in real-time by `live_candle_fetcher.py`)
+- **1h change**: close price from `{coin}_1h` candle table (second-to-last completed 1h candle)
+- **1D change**: close price from `{coin}_1d` candle table (second-to-last completed 1d candle)
+- **Reference value**: mean of daily values over all available historical data. If fewer than `min_data_points` daily data points exist and `fallback_reference` is set, the fallback value is used instead.
+
+## Process Architecture
+
+```
+indicators_fetcher.py (subprocess, runs every 30s)
+    |
+    +---> indicators.py (IndicatorCalculator)
+    |         |
+    |         +---> _data/market_data.db (SQLite candle data)
+    |         +---> _data/indicators.json (config)
+    |
+    +---> _logs/indicators_status.json (output)
+            |
+            +---> main_app.py (MainApp.read_indicators_status)
+                    |
+                    +---> dashboard.py (DashboardRenderer.build_indicators_table)
+```
+
+## File Reference
+
+| File | Description |
+|------|-------------|
+| `_data/indicators.json` | Indicator definitions (config) |
+| `indicators.py` | `IndicatorCalculator` class — computation logic |
+| `indicators_fetcher.py` | Standalone script — runs in a loop, computes indicators, writes JSON |
+| `dashboard.py` | `DashboardRenderer.build_indicators_table()` — renders the table |
+| `main_app.py` | `run_indicators_fetcher()` — process target; `MainApp.read_indicators_status()` — reads JSON |
+| `_logs/indicators_status.json` | Output file with computed indicator values |
+
+## Adding a New Indicator
+
+1. Edit `_data/indicators.json` and add a new entry:
+
+```json
+"my_new_indicator": {
+    "display_name": "My Indicator",
+    "type": "price",
+    "coin": "BTC",
+    "changes": ["1h", "1d"],
+    "show_deviation": true
+}
+```
+
+2. Restart the application (`python main_app.py`). The Indicators Fetcher will automatically pick up the new config on its next run.
+
+No code changes are needed for standard indicator types (`ratio`, `price`, `spread`, `diff_pct`, `ma`, `rsi`). For custom calculations, use the `custom` type.
+
+### Optional Deviation Config Fields
+
+The following optional fields control the deviation reference value:
+
+| Field | Type | Default | Description |
+|-------|------|---------|-------------|
+| `min_data_points` | int | 100 | Minimum number of historical daily data points required before using the computed mean as the reference |
+| `fallback_reference` | float | null | If set and available data points are below `min_data_points`, this value is used as the reference instead of the computed mean |

WIKI/symbol_management.md

@@ -0,0 +1,221 @@
+# Symbol Management Guide
+
+This guide explains how to add or remove Hyperliquid trading symbols (coins) from the trading bot's dashboard, data pipeline, and market cap tracking.
+
+## Overview
+
+The system tracks coins through multiple interconnected components. Each component reads its coin list from a specific source:
+
+| Component | Source | Purpose |
+|-----------|--------|---------|
+| Dashboard display | `WATCHED_COINS` in `main_app.py` | Shows live prices in terminal |
+| Live candle fetcher | `--coins` CLI arg (from `WATCHED_COINS`) | Collects 1-minute candle data |
+| Resampler | `--coins` CLI arg (from `WATCHED_COINS`) | Resamples 1m data to 15+ timeframes |
+| Live price feed | `coins_to_watch` arg (from `WATCHED_COINS`) | WebSocket BBO/trade subscriptions |
+| Market cap fetcher | `coin_id_map.json` | CoinGecko market cap data |
+| Resampling status | `resampling_status.json` | Tracks progress per coin/timeframe |
+| Market cap summary | `market_cap_data.json` | Aggregated market cap snapshots |
+
+## Data Pipeline
+
+```
+Hyperliquid WebSocket
+    |
+    +---> Live Candle Fetcher (1m candles) --> SQLite: {coin}_1m
+    |         |
+    |         +---> Resampler --> SQLite: {coin}_{3m,5m,15m,...,1M}
+    |
+    +---> Live Price Feed (BBO/trades) --> shared_prices dict --> Dashboard
+
+CoinGecko API
+    |
+    +---> Market Cap Fetcher --> SQLite: {coin}_market_cap
+                            --> market_cap_data.json (summary)
+```
+
+All historical data is stored in `_data/market_data.db` (SQLite). Existing data is **preserved** when removing coins; only new data collection stops.
+
+---
+
+## Adding a Symbol
+
+### Step 1: Add to the Watched Coins List
+
+Edit `main_app.py` (line 23):
+
+```python
+WATCHED_COINS = ["BTC", "ETH", "SOL", "BNB", "HYPE", "NEW_COIN", "xyz:BRENTOIL", "xyz:CL"]
+```
+
+### Step 2: Add Display Name (Optional)
+
+If the symbol contains special characters or you want a custom display name, add it to `COIN_DISPLAY_NAMES` in `main_app.py` (lines 25-28):
+
+```python
+COIN_DISPLAY_NAMES = {
+    "xyz:BRENTOIL": "BRENT",
+    "xyz:CL": "WTI",
+    "NEW_COIN": "NewCoin"
+}
+```
+
+### Step 3: Add to Coin ID Map (for Market Cap)
+
+Edit `_data/coin_id_map.json` and add an entry mapping the Hyperliquid symbol to the CoinGecko ID:
+
+```json
+"NEW_COIN": "new-coin-id-on-coingecko"
+```
+
+If the coin is already in the map (e.g., it was previously fetched), skip this step.
+
+### Step 4: Add to Manual Overrides (Optional)
+
+If the CoinGecko ID is ambiguous, add it to the `manual_overrides` dictionary in `coin_id_map.py` (lines 49-61):
+
+```python
+manual_overrides = {
+    "BTC": "bitcoin",
+    "ETH": "ethereum",
+    "NEW_COIN": "new-coin-id-on-coingecko",
+    ...
+}
+```
+
+### Step 5: Restart the Application
+
+Stop all running processes, then start `main_app.py`:
+
+```bash
+python main_app.py
+```
+
+The system will automatically:
+- Create new candle tables in `market_data.db`
+- Begin collecting 1-minute candle data
+- Begin resampling to all timeframes
+- Begin collecting market cap data
+- Display the coin on the dashboard
+
+---
+
+## Removing a Symbol
+
+### Step 1: Stop All Running Processes
+
+Before making changes, stop all Python processes related to the project:
+
+```powershell
+# Find running processes
+Get-WmiObject Win32_Process | Where-Object { $_.ExecutablePath -like "*python*" -and $_.CommandLine -like "*hyper*" }
+
+# Stop them (replace PIDs with actual values)
+Stop-Process -Id <PID1>, <PID2>, ... -Force
+```
+
+### Step 2: Remove from Watched Coins List
+
+Edit `main_app.py` (line 23) and remove the coin from `WATCHED_COINS`:
+
+```python
+WATCHED_COINS = ["BTC", "ETH", "SOL", "BNB", "HYPE", "SUI", "xyz:BRENTOIL", "xyz:CL"]
+```
+
+### Step 3: Remove from Resampling Status
+
+Edit `_data/resampling_status.json` and delete the entire block for the coin, e.g.:
+
+```json
+"REMOVED_COIN": {
+    "12h": { ... },
+    "148m": { ... },
+    ...
+}
+```
+
+### Step 4: Remove from Coin ID Map
+
+Edit `_data/coin_id_map.json` and delete the entry:
+
+```json
+"REMOVED_COIN": "coingecko-id"
+```
+
+### Step 5: Remove from Market Cap Summary
+
+Edit `_data/market_cap_data.json` and delete the entry:
+
+```json
+"REMOVED_COIN_market_cap": { ... }
+```
+
+### Step 6: Remove from Manual Overrides (if present)
+
+Edit `coin_id_map.py` and remove the entry from `manual_overrides`:
+
+```python
+manual_overrides = {
+    "BTC": "bitcoin",
+    "ETH": "ethereum",
+    ...
+}
+```
+
+### Step 7: Restart the Application
+
+```bash
+python main_app.py
+```
+
+**Note:** Existing data in `_data/market_data.db` (candle tables, market cap tables) is **not deleted**. The coin's data remains available for historical analysis; only new data collection stops.
+
+---
+
+## File Reference
+
+### Core Configuration
+
+| File | Line | Description |
+|------|------|-------------|
+| `main_app.py` | 23 | `WATCHED_COINS` list - master coin list for dashboard, candle fetcher, resampler, and live feed |
+| `main_app.py` | 25-28 | `COIN_DISPLAY_NAMES` dict - maps internal symbols to display names |
+| `main_app.py` | 591-594 | `required_timeframes` list - timeframes for resampling |
+
+### Data Files
+
+| File | Description |
+|------|-------------|
+| `_data/market_data.db` | SQLite database with all candle and market cap data. Tables: `{coin}_1m`, `{coin}_{timeframe}`, `{coin}_market_cap` |
+| `_data/resampling_status.json` | Tracks `last_candle_utc` and `total_candles` per coin/timeframe |
+| `_data/coin_id_map.json` | Maps Hyperliquid symbols to CoinGecko IDs for market cap fetching |
+| `_data/market_cap_data.json` | Summary of latest market cap data per coin |
+| `_data/coin_precision.json` | All Hyperliquid coins with trade precision (reference only) |
+| `_data/strategies.json` | Trading strategy configurations (separate from watched coins) |
+
+### Scripts
+
+| File | Description |
+|------|-------------|
+| `main_app.py` | Main orchestrator - starts all processes, renders dashboard |
+| `live_candle_fetcher.py` | Collects 1-minute candles via WebSocket + historical catch-up |
+| `resampler.py` | Resamples 1m candles to multiple timeframes using pandas |
+| `live_market_utils.py` | WebSocket feed for live BBO (best bid/offer) and trade data |
+| `market_cap_fetcher.py` | Fetches daily market cap data from CoinGecko API |
+| `coin_id_map.py` | Generates `coin_id_map.json` from Hyperliquid + CoinGecko APIs |
+| `dashboard_data_fetcher.py` | Fetches account balances and positions for dashboard |
+
+---
+
+## Important Notes
+
+1. **Always stop processes before editing config files.** Running processes will overwrite changes to `resampling_status.json` and `market_data.db`.
+
+2. **Existing data is preserved.** Removing a coin from the lists stops new data collection but does not delete existing data from the SQLite database.
+
+3. **The `coin_id_map.json` is auto-generated.** Running `python coin_id_map.py` regenerates it from the Hyperliquid API. Manual overrides in `coin_id_map.py` ensure correct CoinGecko mappings.
+
+4. **Market cap fetcher is not auto-started.** The market cap fetcher process is currently disabled in `main_app.py` (line 614). It can be run manually: `python market_cap_fetcher.py`.
+
+5. **Strategy coins are separate.** Trading strategies in `_data/strategies.json` define their own coins independently of `WATCHED_COINS`. A coin can be traded by a strategy even if it's not in the watched list.
+
+6. **Special symbols.** Coins with the `xyz:` prefix (e.g., `xyz:BRENTOIL`, `xyz:CL`) are synthetic/derivative symbols on Hyperliquid. They follow the same management process as regular coins.

_data/coin_id_map.json

@@ -16,7 +16,6 @@
     "AR": "arweave",
     "ARB": "osmosis-allarb",
     "ARK": "ark-3",
-    "ASTER": "astar",
     "ATOM": "lost-bitcoin-layer",
     "AVAX": "binance-peg-avalanche",
     "AVNT": "avantis",
@@ -139,7 +138,6 @@
     "POPCAT": "popcat",
     "PROMPT": "wayfinder",
     "PROVE": "succinct",
-    "PUMP": "pump-fun",
     "PURR": "purr-2",
     "PYTH": "pyth-network",
     "RDNT": "radiant-capital",
@@ -198,7 +196,6 @@
     "XRP": "ripple",
     "YGG": "yield-guild-games",
     "YZY": "yzy",
-    "ZEC": "zcash",
     "ZEN": "zenith-3",
     "ZEREBRO": "zerebro",
     "ZETA": "zeta",

_data/indicators.json

@@ -0,0 +1,32 @@
+{
+    "wti_brent_ratio": {
+        "display_name": "WTI/BRENT",
+        "type": "ratio",
+        "numerator": "xyz:CL",
+        "denominator": "xyz:BRENTOIL",
+        "changes": ["1h", "1d"],
+        "show_deviation": true,
+        "min_data_points": 100,
+        "fallback_reference": 0.96065
+    },
+    "gold_silver_ratio": {
+        "display_name": "GOLD/SILVER",
+        "type": "ratio",
+        "numerator": "xyz:GOLD",
+        "denominator": "xyz:SILVER",
+        "changes": ["1h", "1d"],
+        "show_deviation": true,
+        "min_data_points": 100,
+        "fallback_reference": 61.59
+    },
+    "xyz100_ustech_ratio": {
+        "display_name": "XYZ100/USTECH",
+        "type": "ratio",
+        "numerator": "xyz:XYZ100",
+        "denominator": "mkts:USTECH",
+        "changes": ["1h", "1d"],
+        "show_deviation": true,
+        "min_data_points": 100,
+        "fallback_reference": 41.10
+    }
+}

_data/market_cap_data.json

@@ -84,11 +84,6 @@
         "timestamp_ms": 1762214400000,
         "market_cap": 411547691.74511635
     },
-    "ASTER_market_cap": {
-        "datetime_utc": "2025-11-04 00:00:00",
-        "timestamp_ms": 1762214400000,
-        "market_cap": 122331099.54500043
-    },
     "ATOM_market_cap": {
         "datetime_utc": "2025-11-04 00:00:00",
         "timestamp_ms": 1762214400000,
@@ -699,11 +694,6 @@
         "timestamp_ms": 1762214400000,
         "market_cap": 116187315.47981949
     },
-    "PUMP_market_cap": {
-        "datetime_utc": "2025-11-04 00:00:00",
-        "timestamp_ms": 1762214400000,
-        "market_cap": 1369591728.1563232
-    },
     "PURR_market_cap": {
         "datetime_utc": "2025-11-04 00:00:00",
         "timestamp_ms": 1762214400000,
@@ -994,11 +984,6 @@
         "timestamp_ms": 1762214400000,
         "market_cap": 49793986.29032182
     },
-    "ZEC_market_cap": {
-        "datetime_utc": "2025-11-04 00:00:00",
-        "timestamp_ms": 1762214400000,
-        "market_cap": 6917445577.244665
-    },
     "ZEN_market_cap": {
         "datetime_utc": "2025-11-04 00:00:00",
         "timestamp_ms": 1762214400000,

coin_id_map.py

@@ -52,9 +52,6 @@ def update_coin_mapping():
         "SOL": "solana",
         "BNB": "binancecoin",
         "HYPE": "hyperliquid",
-        "PUMP": "pump-fun",
-        "ASTER": "astar",
-        "ZEC": "zcash",
         "SUI": "sui",
         "ACE": "endurance",
         # Add other important ones you watch here

dashboard.py

@@ -0,0 +1,347 @@
+"""
+Dashboard rendering module using rich.
+Provides DashboardRenderer for building rich terminal tables and layouts.
+"""
+
+from datetime import datetime, timezone
+
+try:
+    from rich.console import Console
+    from rich.table import Table
+    from rich.live import Live
+    from rich.layout import Layout
+    from rich.text import Text
+    from rich.padding import Padding
+    RICH_AVAILABLE = True
+except ImportError:
+    RICH_AVAILABLE = False
+
+
+class DashboardRenderer:
+    """Encapsulates all rich-based dashboard rendering logic."""
+
+    def __init__(self, console=None, table_visibility=None):
+        if not RICH_AVAILABLE:
+            raise ImportError("rich is not available. Install with: pip install rich")
+        self.console = console or Console()
+        self.previous_prices = {}
+        self.table_visibility = table_visibility or {
+            "market": True,
+            "strategies": False,
+            "indicators": True,
+            "balances": True,
+        }
+
+    def toggle_table(self, table_name, enabled=None):
+        """Toggle a table's visibility on the dashboard.
+
+        Args:
+            table_name: The key of the table to toggle (e.g. "market", "strategies").
+            enabled: If None, flips the current state. Otherwise sets to the given value.
+
+        Returns:
+            The new visibility state for the table.
+        """
+        if table_name not in self.table_visibility:
+            raise ValueError(f"Unknown table: {table_name}")
+        if enabled is None:
+            self.table_visibility[table_name] = not self.table_visibility[table_name]
+        else:
+            self.table_visibility[table_name] = enabled
+        return self.table_visibility[table_name]
+
+    def _format_price(self, price_val, width=10):
+        """Format a price value with appropriate precision."""
+        try:
+            price_float = float(price_val)
+            if price_float < 1:
+                return f"{price_float:>{width}.6f}"
+            elif price_float < 100:
+                return f"{price_float:>{width}.4f}"
+            else:
+                return f"{price_float:>{width}.2f}"
+        except (ValueError, TypeError):
+            return f"{'Loading...':>{width}}"
+
+    def build_market_table(self, watched_coins, prices, display_names):
+        """Build the market dashboard table."""
+        table = Table(title="Market Dashboard", show_header=True, header_style="bold cyan", title_style="bold white")
+        table.add_column("#", justify="right", style="dim", width=3)
+        table.add_column("Coin", justify="center", width=8)
+        table.add_column("Best Bid", justify="right")
+        table.add_column("Live Price", justify="right")
+        table.add_column("Best Ask", justify="right")
+        table.add_column("Gap", justify="right")
+        table.add_column("Dir", justify="center", width=3)
+
+        for i, coin in enumerate(watched_coins, 1):
+            display_name = display_names.get(coin, coin)
+            mid = prices.get(coin)
+            bid = prices.get(f"{coin}_bid")
+            ask = prices.get(f"{coin}_ask")
+
+            formatted_mid = self._format_price(mid)
+            formatted_bid = self._format_price(bid)
+            formatted_ask = self._format_price(ask)
+
+            gap_str = "Loading..."
+            gap_style = "dim"
+            try:
+                gap_val = float(ask) - float(bid)
+                if gap_val < 1:
+                    gap_str = f"{gap_val:.6f}"
+                else:
+                    gap_str = f"{gap_val:.4f}"
+                gap_style = "green" if gap_val > 0 else "red"
+            except (ValueError, TypeError):
+                pass
+
+            direction = " "
+            direction_style = "dim"
+            prev_mid = self.previous_prices.get(coin)
+            if prev_mid is not None and mid is not None:
+                try:
+                    if float(mid) > float(prev_mid):
+                        direction = "↑"
+                        direction_style = "green"
+                    elif float(mid) < float(prev_mid):
+                        direction = "↓"
+                        direction_style = "red"
+                except (ValueError, TypeError):
+                    pass
+
+            table.add_row(
+                str(i), display_name, formatted_bid, formatted_mid, formatted_ask,
+                Text(gap_str, style=gap_style),
+                Text(direction, style=direction_style)
+            )
+
+            if coin == "SUI":
+                table.add_section()
+
+            if mid is not None:
+                self.previous_prices[coin] = mid
+
+        return table
+
+    def build_strategy_table(self, strategy_statuses, strategy_configs):
+        """Build the strategies table."""
+        table = Table(title="Strategies", show_header=True, header_style="bold cyan", title_style="bold white")
+        table.add_column("#", justify="center", width=3)
+        table.add_column("Strategy Name", width=25)
+        table.add_column("Coin", justify="center", width=8)
+        table.add_column("Signal", justify="center", width=10)
+        table.add_column("Signal Price", justify="right", width=14)
+        table.add_column("Last Change", justify="right", width=19)
+        table.add_column("TF", justify="center", width=7)
+        table.add_column("Size", justify="center", width=10)
+
+        for i, (name, status) in enumerate(strategy_statuses.items(), 1):
+            signal = status.get('current_signal', 'N/A')
+            price = status.get('signal_price')
+            price_display = f"{price:.4f}" if isinstance(price, (int, float)) else "-"
+            last_change = status.get('last_signal_change_utc')
+            last_change_display = 'Never'
+            if last_change:
+                dt_utc = datetime.fromisoformat(last_change.replace('Z', '+00:00')).replace(tzinfo=timezone.utc)
+                dt_local = dt_utc.astimezone(None)
+                last_change_display = dt_local.strftime('%Y-%m-%d %H:%M')
+
+            config_params = strategy_configs.get(name, {}).get('parameters', {})
+            coin = status.get('coin', config_params.get('coin', 'N/A'))
+
+            size = status.get('size')
+            if not size:
+                if 'coins_to_copy' in config_params:
+                    size = 'Multi'
+                else:
+                    size = config_params.get('size', 'N/A')
+
+            timeframe = config_params.get('timeframe', 'N/A')
+
+            signal_style = ""
+            if signal == "BUY":
+                signal_style = "green"
+            elif signal == "SELL":
+                signal_style = "red"
+            elif signal == "NEUTRAL":
+                signal_style = "yellow"
+
+            table.add_row(
+                str(i), name, coin,
+                Text(signal, style=signal_style) if signal_style else signal,
+                price_display, last_change_display, timeframe, str(size)
+            )
+
+        return table
+
+    def _format_change_value(self, value):
+        """Format a percentage change value with color styling."""
+        if value is None:
+            return Text("N/A", style="dim")
+        if value > 0:
+            return Text(f"+{value:.2f}%", style="green")
+        elif value < 0:
+            return Text(f"{value:.2f}%", style="red")
+        else:
+            return Text(f"{value:.2f}%", style="yellow")
+
+    def _format_value(self, value, width=12):
+        """Format a numeric value for display."""
+        if value is None:
+            return Text("N/A", style="dim")
+        try:
+            val = float(value)
+            if abs(val) < 1:
+                return Text(f"{val:>{width}.6f}")
+            elif abs(val) < 100:
+                return Text(f"{val:>{width}.4f}")
+            else:
+                return Text(f"{val:>{width}.2f}")
+        except (ValueError, TypeError):
+            return Text("N/A", style="dim")
+
+    def build_indicators_table(self, indicators_status):
+        """Build the indicators dashboard table."""
+        table = Table(title="Indicators", show_header=True, header_style="bold cyan", title_style="bold white")
+        table.add_column("#", justify="right", style="dim", width=3)
+        table.add_column("Indicator", width=20)
+        table.add_column("Value", justify="right")
+        table.add_column("1h Change", justify="right", width=12)
+        table.add_column("1D Change", justify="right", width=12)
+        table.add_column("Deviation", justify="right", width=12)
+
+        if not indicators_status:
+            table.add_row("1", "Loading...", "N/A", "N/A", "N/A", "N/A")
+            return table
+
+        indicators = indicators_status.get("indicators", {})
+        for i, (name, data) in enumerate(indicators.items(), 1):
+            display_name = data.get("display_name", name)
+            value = data.get("value")
+            changes = data.get("changes", {})
+            deviation = data.get("deviation")
+
+            formatted_value = self._format_value(value)
+            change_1h = self._format_change_value(changes.get("1h"))
+            change_1d = self._format_change_value(changes.get("1d"))
+
+            if deviation is not None:
+                if deviation > 0:
+                    deviation_str = Text(f"+{deviation:.2f}%", style="green")
+                elif deviation < 0:
+                    deviation_str = Text(f"{deviation:.2f}%", style="red")
+                else:
+                    deviation_str = Text(f"{deviation:.2f}%", style="yellow")
+            else:
+                deviation_str = Text("N/A", style="dim")
+
+            table.add_row(
+                str(i), display_name, formatted_value,
+                change_1h, change_1d, deviation_str
+            )
+
+        return table
+
+    def build_balances_table(self, account_data, prices=None):
+        """Build a combined balances and open positions table.
+
+        Args:
+            account_data: dict with keys:
+                - spot_balances: list of {coin, total}
+                - positions: list of position dicts with position data
+                - account_value: float
+                - margin_used: float
+                - utilization: float
+            prices: dict mapping coin names to current mark prices
+        """
+        if prices is None:
+            prices = {}
+        table = Table(show_header=True, header_style="bold cyan", title="Account Summary")
+        table.add_column("Type", justify="center", width=8)
+        table.add_column("Coin", justify="center", width=8)
+        table.add_column("Size", justify="right", width=12)
+        table.add_column("Value", justify="right", width=12)
+
+        spot_balances = account_data.get('spot_balances', [])
+        for bal in spot_balances:
+            total = float(bal.get('total', 0))
+            if total > 0:
+                coin = bal.get('coin', 'Unknown')
+                mark_price = float(prices.get(coin, 0))
+                usd_value = total * mark_price
+                table.add_row(
+                    Text("Spot", style="blue"),
+                    coin,
+                    f"{total:,.4f}",
+                    f"${usd_value:,.2f}"
+                )
+
+        positions = account_data.get('positions', [])
+        for pos in positions:
+            position = pos.get('position', {})
+            coin = position.get('coin', 'Unknown')
+            size = float(position.get('szi', 0))
+            if size != 0:
+                position_value = float(position.get('positionValue', 0))
+                side = "LONG" if size > 0 else "SHORT"
+                side_style = "green" if size > 0 else "red"
+
+                table.add_row(
+                    Text(f"P({side})", style=side_style),
+                    coin,
+                    f"{size:,.4f}",
+                    f"${position_value:,.2f}"
+                )
+
+        if not spot_balances and not positions:
+            table.add_row("None", "-", "-", "-")
+
+        account_value = account_data.get('account_value', 0)
+        margin_used = account_data.get('margin_used', 0)
+        utilization = account_data.get('utilization', 0)
+
+# table.add_section()
+        table.add_row(
+            Text("Acct", style="bold"),
+            "-", "-",
+            f"${account_value:,.2f}"
+        )
+        table.add_row(
+            Text("Util", style="bold"),
+            "-", "-",
+            f"{utilization:.2f}%"
+        )
+
+        return table
+
+    def build_layout(self, watched_coins, prices, display_names, strategy_statuses, strategy_configs, indicators_status=None, account_data=None):
+        """Build the complete dashboard layout in a 2x2 grid."""
+        from rich.layout import Layout as RichLayout
+
+        tables = []
+
+        if self.table_visibility.get("market", True):
+            tables.append(self.build_market_table(watched_coins, prices, display_names))
+        if self.table_visibility.get("indicators", True):
+            tables.append(Padding(self.build_indicators_table(indicators_status), (0, 0, 0, 2)))
+        if account_data is not None and self.table_visibility.get("balances", True):
+            tables.append(Padding(self.build_balances_table(account_data, prices), (0, 0, 0, 2)))
+        if self.table_visibility.get("strategies", True):
+            tables.append(self.build_strategy_table(strategy_statuses, strategy_configs))
+
+        if not tables:
+            return RichLayout()
+
+        if len(tables) <= 2:
+            layout = RichLayout()
+            layout.split_row(*tables)
+            return layout
+
+        top = RichLayout(ratio=1)
+        bottom = RichLayout(ratio=2)
+        top.split_row(*tables[:2])
+        bottom.split_row(*tables[2:])
+        layout = RichLayout()
+        layout.split_column(top, bottom)
+        return layout

data_fetcher.py

@@ -175,7 +175,7 @@ if __name__ == "__main__":
     parser.add_argument(
         "--coins",
         nargs='+',
-        default=["BTC", "ETH", "xyz:BRENTOIL", "xyz:CL"],
+        default=["BTC", "ETH", "xyz:BRENTOIL", "xyz:CL", "xyz:GOLD", "xyz:SILVER"],
         help="List of coins to fetch (e.g., BTC ETH), or 'all' to fetch all coins."
     )
     parser.add_argument("--interval", default="1m", help="Candle interval (e.g., 1m, 5m, 1h).")

fetch_history.py

@@ -0,0 +1,101 @@
+import requests
+import json
+import sqlite3
+import time
+from datetime import datetime, timezone
+
+DB_PATH = "_data/market_data.db"
+URL = "https://api.hyperliquid.xyz/info"
+
+def fetch_historical_candles(coin, start_ms, end_ms, interval="1m"):
+    """Fetch historical candles using the raw HTTP API."""
+    candles = []
+    current_start = start_ms
+    while current_start < end_ms:
+        payload = {
+            "type": "candleSnapshot",
+            "req": {
+                "coin": coin,
+                "interval": interval,
+                "startTime": current_start,
+                "endTime": end_ms
+            }
+        }
+        resp = requests.post(URL, json=payload)
+        batch = resp.json()
+        if not batch:
+            break
+        for candle in batch:
+            candle['coin'] = coin
+            candles.append(candle)
+        last_ts = batch[-1]['t']
+        if last_ts < current_start:
+            break
+        current_start = last_ts + 1
+        time.sleep(0.5)
+    return candles
+
+def write_candles_to_db(coin, candles, interval="1m"):
+    """Write candles to the database."""
+    table_name = coin + "_" + interval
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    # Ensure table exists
+    cursor.execute(f'''
+        CREATE TABLE IF NOT EXISTS "{table_name}" (
+            datetime_utc TEXT,
+            timestamp_ms INTEGER PRIMARY KEY,
+            open REAL,
+            high REAL,
+            low REAL,
+            close REAL,
+            volume REAL,
+            number_of_trades INTEGER
+        )
+    ''')
+    for candle in candles:
+        record = (
+            datetime.fromtimestamp(candle['t'] / 1000, tz=timezone.utc).strftime('%Y-%m-%d %H:%M:%S'),
+            candle['t'],
+            candle.get('o'), candle.get('h'), candle.get('l'), candle.get('c'),
+            candle.get('v'), candle.get('n')
+        )
+        cursor.execute(f'''
+            INSERT OR REPLACE INTO "{table_name}" (datetime_utc, timestamp_ms, open, high, low, close, volume, number_of_trades)
+            VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+        ''', record)
+    conn.commit()
+    conn.close()
+
+def get_last_timestamp(coin):
+    """Get the most recent timestamp from the database."""
+    table_name = coin + "_1m"
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    try:
+        cursor.execute(f'SELECT MAX(timestamp_ms) FROM "{table_name}"')
+        result = cursor.fetchone()
+        return int(result[0]) if result and result[0] is not None else None
+    except:
+        return None
+    finally:
+        conn.close()
+
+coins = ["mkts:USTECH", "xyz:XYZ100"]
+now_ms = int(time.time() * 1000)
+seven_days_ms = 7 * 24 * 60 * 60 * 1000
+
+for coin in coins:
+    for tf in ["1m", "1d"]:
+        start_ts = now_ms - seven_days_ms
+        if start_ts >= now_ms:
+            print(f"{coin} ({tf}): Already up to date")
+            continue
+
+        print(f"{coin} ({tf}): Fetching historical candles from {datetime.fromtimestamp(start_ts/1000, tz=timezone.utc)} to {datetime.fromtimestamp(now_ms/1000, tz=timezone.utc)}...")
+        candles = fetch_historical_candles(coin, start_ts, now_ms, interval=tf)
+        print(f"{coin} ({tf}): Fetched {len(candles)} candles")
+        write_candles_to_db(coin, candles, interval=tf)
+        print(f"{coin} ({tf}): Written to database")
+
+print("Done!")

indicators.py

@@ -0,0 +1,445 @@
+"""
+Indicator calculation module.
+Provides IndicatorCalculator for computing various financial indicators
+from SQLite candle data, including ratios, prices, moving averages, RSI,
+and custom functions.
+"""
+
+import json
+import os
+import sqlite3
+import importlib
+import logging
+import pandas as pd
+import numpy as np
+
+
+class IndicatorCalculator:
+    """
+    Computes indicator values from SQLite candle data.
+    Supports ratio, price, spread, diff_pct, ma, rsi, and custom types.
+    """
+
+    def __init__(self, config_path, db_path):
+        self.config_path = config_path
+        self.db_path = db_path
+        self.config = self._load_config()
+
+    def _load_config(self):
+        """Load indicator definitions from JSON config file."""
+        try:
+            with open(self.config_path, 'r', encoding='utf-8') as f:
+                return json.load(f)
+        except (FileNotFoundError, json.JSONDecodeError) as e:
+            logging.error(f"Failed to load indicators config from '{self.config_path}': {e}")
+            return {}
+
+    def _get_latest_close(self, coin, timeframe="1m"):
+        """Get the latest close price from a candle table."""
+        table = f"{coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT close FROM "{table}" ORDER BY timestamp_ms DESC LIMIT 1'
+                ).fetchone()
+                return float(result[0]) if result and result[0] is not None else None
+        except Exception as e:
+            logging.debug(f"Could not get latest close for {coin} ({timeframe}): {e}")
+            return None
+
+    def _get_close_n_candles_ago(self, coin, timeframe, n=1):
+        """Get the close price from n candles ago (n=1 = most recent completed candle)."""
+        table = f"{coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT close FROM "{table}" ORDER BY timestamp_ms DESC LIMIT 1 OFFSET {n}'
+                ).fetchone()
+                return float(result[0]) if result and result[0] is not None else None
+        except Exception as e:
+            logging.debug(f"Could not get close {n} candles ago for {coin} ({timeframe}): {e}")
+            return None
+
+    def _get_all_closes(self, coin, timeframe="1d"):
+        """Get all close prices from a candle table, ordered by time."""
+        table = f"{coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT close FROM "{table}" ORDER BY timestamp_ms'
+                ).fetchall()
+                return [float(r[0]) for r in result if r[0] is not None]
+        except Exception as e:
+            logging.debug(f"Could not get all closes for {coin} ({timeframe}): {e}")
+            return []
+
+    def _get_all_ratio(self, num_coin, den_coin, timeframe="1d"):
+        """Get all ratio values (num/den) from candle tables, ordered by time."""
+        num_table = f"{num_coin}_{timeframe}"
+        den_table = f"{den_coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT n.close / d.close as ratio '
+                    f'FROM "{num_table}" n '
+                    f'JOIN "{den_table}" d ON n.timestamp_ms = d.timestamp_ms '
+                    f'ORDER BY n.timestamp_ms'
+                ).fetchall()
+                return [float(r[0]) for r in result if r[0] is not None]
+        except Exception as e:
+            logging.debug(f"Could not get ratio series for {num_coin}/{den_coin} ({timeframe}): {e}")
+            return []
+
+    def _get_all_spread(self, num_coin, den_coin, timeframe="1d"):
+        """Get all spread values (num - den) from candle tables, ordered by time."""
+        num_table = f"{num_coin}_{timeframe}"
+        den_table = f"{den_coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT n.close - d.close as spread '
+                    f'FROM "{num_table}" n '
+                    f'JOIN "{den_table}" d ON n.timestamp_ms = d.timestamp_ms '
+                    f'ORDER BY n.timestamp_ms'
+                ).fetchall()
+                return [float(r[0]) for r in result if r[0] is not None]
+        except Exception as e:
+            logging.debug(f"Could not get spread series for {num_coin}/{den_coin} ({timeframe}): {e}")
+            return []
+
+    def _get_all_diff_pct(self, num_coin, den_coin, timeframe="1d"):
+        """Get all percentage difference values ((num-den)/den*100) from candle tables."""
+        num_table = f"{num_coin}_{timeframe}"
+        den_table = f"{den_coin}_{timeframe}"
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                result = conn.execute(
+                    f'SELECT (n.close - d.close) / d.close * 100 as diff_pct '
+                    f'FROM "{num_table}" n '
+                    f'JOIN "{den_table}" d ON n.timestamp_ms = d.timestamp_ms '
+                    f'ORDER BY n.timestamp_ms'
+                ).fetchall()
+                return [float(r[0]) for r in result if r[0] is not None]
+        except Exception as e:
+            logging.debug(f"Could not get diff_pct series for {num_coin}/{den_coin} ({timeframe}): {e}")
+            return []
+
+    def _compute_ma(self, closes, period):
+        """Compute Simple Moving Average using pandas."""
+        if len(closes) < period:
+            return []
+        series = pd.Series(closes)
+        ma = series.rolling(window=period).mean()
+        return ma.dropna().tolist()
+
+    def _compute_rsi(self, closes, period):
+        """Compute RSI using Wilder's smoothing method."""
+        if len(closes) < period + 1:
+            return []
+        series = pd.Series(closes)
+        delta = series.diff()
+        gain = delta.where(delta > 0, 0)
+        loss = (-delta).where(delta < 0, 0)
+        avg_gain = gain.rolling(window=period, min_periods=period).mean()
+        avg_loss = loss.rolling(window=period, min_periods=period).mean()
+        rs = avg_gain / avg_loss.replace(0, np.nan)
+        rsi = 100 - (100 / (1 + rs))
+        return rsi.dropna().tolist()
+
+    def _get_ma_value(self, coin, timeframe, period, n_candles_ago=0):
+        """Get MA value from n candles ago (0 = latest, 1 = second-to-last)."""
+        closes = self._get_all_closes(coin, timeframe)
+        if not closes:
+            return None
+        ma_values = self._compute_ma(closes, period)
+        if not ma_values:
+            return None
+        if n_candles_ago < len(ma_values):
+            return ma_values[-(1 + n_candles_ago)]
+        return None
+
+    def _get_rsi_value(self, coin, timeframe, period, n_candles_ago=0):
+        """Get RSI value from n candles ago (0 = latest, 1 = second-to-last)."""
+        closes = self._get_all_closes(coin, timeframe)
+        if not closes:
+            return None
+        rsi_values = self._compute_rsi(closes, period)
+        if not rsi_values:
+            return None
+        if n_candles_ago < len(rsi_values):
+            return rsi_values[-(1 + n_candles_ago)]
+        return None
+
+    def _format_change(self, current, past):
+        """Compute percentage change between two values."""
+        if past is None or past == 0 or current is None:
+            return None
+        return (current - past) / past * 100
+
+    def calculate_indicator(self, ind_def):
+        """
+        Calculate a single indicator based on its definition.
+        Returns a dict with value, changes, reference, and deviation.
+        """
+        ind_type = ind_def.get("type", "price")
+
+        if ind_type == "ratio":
+            return self._calc_ratio(ind_def)
+        elif ind_type == "price":
+            return self._calc_price(ind_def)
+        elif ind_type == "spread":
+            return self._calc_spread(ind_def)
+        elif ind_type == "diff_pct":
+            return self._calc_diff_pct(ind_def)
+        elif ind_type == "ma":
+            return self._calc_ma(ind_def)
+        elif ind_type == "rsi":
+            return self._calc_rsi(ind_def)
+        elif ind_type == "custom":
+            return self._calc_custom(ind_def)
+        else:
+            logging.warning(f"Unknown indicator type: {ind_type}")
+            return None
+
+    def _calc_ratio(self, ind_def):
+        """Calculate a ratio indicator (numerator / denominator)."""
+        num = ind_def["numerator"]
+        den = ind_def["denominator"]
+
+        num_now = self._get_latest_close(num)
+        den_now = self._get_latest_close(den)
+        if num_now is None or den_now is None or den_now == 0:
+            return None
+        current = num_now / den_now
+
+        changes = {}
+        for period in ind_def.get("changes", []):
+            num_past = self._get_close_n_candles_ago(num, period, n=1)
+            den_past = self._get_close_n_candles_ago(den, period, n=1)
+            if num_past is not None and den_past is not None and den_past != 0:
+                past = num_past / den_past
+                changes[period] = self._format_change(current, past)
+            else:
+                changes[period] = None
+
+        reference = None
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            ratios = self._get_all_ratio(num, den, "1d")
+            if ratios:
+                min_points = ind_def.get("min_data_points", 100)
+                fallback_ref = ind_def.get("fallback_reference")
+                if len(ratios) < min_points and fallback_ref is not None:
+                    reference = fallback_ref
+                else:
+                    reference = sum(ratios) / len(ratios)
+                deviation = self._format_change(current, reference)
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_price(self, ind_def):
+        """Calculate a single price indicator."""
+        coin = ind_def["coin"]
+
+        current = self._get_latest_close(coin)
+        if current is None:
+            return None
+
+        changes = {}
+        for period in ind_def.get("changes", []):
+            past = self._get_close_n_candles_ago(coin, period, n=1)
+            changes[period] = self._format_change(current, past)
+
+        reference = None
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            closes = self._get_all_closes(coin, "1d")
+            if closes:
+                min_points = ind_def.get("min_data_points", 100)
+                fallback_ref = ind_def.get("fallback_reference")
+                if len(closes) < min_points and fallback_ref is not None:
+                    reference = fallback_ref
+                else:
+                    reference = sum(closes) / len(closes)
+                deviation = self._format_change(current, reference)
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_spread(self, ind_def):
+        """Calculate a spread indicator (numerator - denominator)."""
+        num = ind_def["numerator"]
+        den = ind_def["denominator"]
+
+        num_now = self._get_latest_close(num)
+        den_now = self._get_latest_close(den)
+        if num_now is None or den_now is None:
+            return None
+        current = num_now - den_now
+
+        changes = {}
+        for period in ind_def.get("changes", []):
+            num_past = self._get_close_n_candles_ago(num, period, n=1)
+            den_past = self._get_close_n_candles_ago(den, period, n=1)
+            if num_past is not None and den_past is not None:
+                past = num_past - den_past
+                changes[period] = self._format_change(current, past)
+            else:
+                changes[period] = None
+
+        reference = None
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            spreads = self._get_all_spread(num, den, "1d")
+            if spreads:
+                min_points = ind_def.get("min_data_points", 100)
+                fallback_ref = ind_def.get("fallback_reference")
+                if len(spreads) < min_points and fallback_ref is not None:
+                    reference = fallback_ref
+                else:
+                    reference = sum(spreads) / len(spreads)
+                deviation = self._format_change(current, reference)
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_diff_pct(self, ind_def):
+        """Calculate a percentage difference indicator ((num-den)/den*100)."""
+        num = ind_def["numerator"]
+        den = ind_def["denominator"]
+
+        num_now = self._get_latest_close(num)
+        den_now = self._get_latest_close(den)
+        if num_now is None or den_now is None or den_now == 0:
+            return None
+        current = (num_now - den_now) / den_now * 100
+
+        changes = {}
+        for period in ind_def.get("changes", []):
+            num_past = self._get_close_n_candles_ago(num, period, n=1)
+            den_past = self._get_close_n_candles_ago(den, period, n=1)
+            if num_past is not None and den_past is not None and den_past != 0:
+                past = (num_past - den_past) / den_past * 100
+                changes[period] = self._format_change(current, past)
+            else:
+                changes[period] = None
+
+        reference = None
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            diffs = self._get_all_diff_pct(num, den, "1d")
+            if diffs:
+                min_points = ind_def.get("min_data_points", 100)
+                fallback_ref = ind_def.get("fallback_reference")
+                if len(diffs) < min_points and fallback_ref is not None:
+                    reference = fallback_ref
+                else:
+                    reference = sum(diffs) / len(diffs)
+                deviation = self._format_change(current, reference)
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_ma(self, ind_def):
+        """Calculate a moving average indicator."""
+        coin = ind_def["coin"]
+        timeframe = ind_def.get("timeframe", "1h")
+        period = ind_def.get("period", 20)
+
+        current = self._get_ma_value(coin, timeframe, period, n_candles_ago=0)
+        if current is None:
+            return None
+
+        changes = {}
+        for period_label in ind_def.get("changes", []):
+            if period_label == "1h":
+                past = self._get_ma_value(coin, "1h", period, n_candles_ago=1)
+            elif period_label == "1d":
+                past = self._get_ma_value(coin, "1d", period, n_candles_ago=1)
+            else:
+                past = self._get_ma_value(coin, period_label, period, n_candles_ago=1)
+            changes[period_label] = self._format_change(current, past)
+
+        reference = None
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            live_price = self._get_latest_close(coin)
+            if live_price is not None and current != 0:
+                reference = current
+                deviation = (live_price - current) / current * 100
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_rsi(self, ind_def):
+        """Calculate an RSI indicator."""
+        coin = ind_def["coin"]
+        timeframe = ind_def.get("timeframe", "1h")
+        period = ind_def.get("period", 14)
+
+        current = self._get_rsi_value(coin, timeframe, period, n_candles_ago=0)
+        if current is None:
+            return None
+
+        changes = {}
+        for period_label in ind_def.get("changes", []):
+            if period_label == "1h":
+                past = self._get_rsi_value(coin, "1h", period, n_candles_ago=1)
+            elif period_label == "1d":
+                past = self._get_rsi_value(coin, "1d", period, n_candles_ago=1)
+            else:
+                past = self._get_rsi_value(coin, period_label, period, n_candles_ago=1)
+            if past is not None:
+                changes[period_label] = current - past
+            else:
+                changes[period_label] = None
+
+        reference = 50.0
+        deviation = None
+        if ind_def.get("show_deviation", False):
+            deviation = current - 50.0
+
+        return {"value": current, "reference": reference, "changes": changes, "deviation": deviation}
+
+    def _calc_custom(self, ind_def):
+        """Calculate a custom indicator by calling a user-defined function."""
+        module_path = ind_def.get("module")
+        function_name = ind_def.get("function")
+        args = ind_def.get("args", {})
+
+        if not module_path or not function_name:
+            logging.error(f"Custom indicator missing 'module' or 'function': {ind_def}")
+            return None
+
+        try:
+            module = importlib.import_module(module_path)
+            func = getattr(module, function_name)
+        except (ImportError, AttributeError) as e:
+            logging.error(f"Failed to load custom indicator {module_path}.{function_name}: {e}")
+            return None
+
+        try:
+            result = func(self.db_path, **args)
+            if not isinstance(result, dict):
+                logging.error(f"Custom indicator {function_name} must return a dict, got {type(result)}")
+                return None
+            return result
+        except Exception as e:
+            logging.error(f"Custom indicator {function_name} raised an error: {e}", exc_info=True)
+            return None
+
+    def calculate_all(self):
+        """Calculate all indicators defined in the config file."""
+        results = {}
+        for name, ind_def in self.config.items():
+            result = self.calculate_indicator(ind_def)
+            if result:
+                results[name] = {
+                    "display_name": ind_def.get("display_name", name),
+                    **result
+                }
+            else:
+                results[name] = {
+                    "display_name": ind_def.get("display_name", name),
+                    "value": None,
+                    "reference": None,
+                    "changes": {},
+                    "deviation": None
+                }
+        return results

indicators_fetcher.py

@@ -0,0 +1,86 @@
+"""
+Indicators Data Fetcher
+
+A standalone process that runs in a loop to compute financial indicators
+(ratios, prices, MAs, RSI, custom) from SQLite candle data and save
+the results to a JSON status file for the main dashboard to display.
+
+Follows the same pattern as dashboard_data_fetcher.py.
+"""
+
+import logging
+import os
+import sys
+import json
+import time
+import argparse
+from datetime import datetime, timezone
+
+from logging_utils import setup_logging
+from indicators import IndicatorCalculator
+
+
+class IndicatorsFetcher:
+    """
+    Periodically computes all configured indicators and saves them to a JSON file.
+    """
+
+    def __init__(self, log_level: str):
+        setup_logging(log_level, 'IndicatorsFetcher')
+
+        project_root = os.path.dirname(os.path.abspath(__file__))
+        self.db_path = os.path.join(project_root, "_data", "market_data.db")
+        self.config_path = os.path.join(project_root, "_data", "indicators.json")
+        self.status_file_path = os.path.join(project_root, "_logs", "indicators_status.json")
+
+        self.calculator = IndicatorCalculator(
+            config_path=self.config_path,
+            db_path=self.db_path
+        )
+
+        logging.info(f"Indicators Fetcher initialized. DB: {self.db_path}, Config: {self.config_path}")
+
+    def fetch_and_save_indicators(self):
+        """Compute all indicators and save to JSON status file."""
+        try:
+            results = self.calculator.calculate_all()
+
+            status = {
+                "last_updated_utc": datetime.now(timezone.utc).isoformat(),
+                "indicators": results
+            }
+
+            logs_dir = os.path.dirname(self.status_file_path)
+            os.makedirs(logs_dir, exist_ok=True)
+
+            temp_file_path = self.status_file_path + ".tmp"
+            with open(temp_file_path, 'w', encoding='utf-8') as f:
+                json.dump(status, f, indent=4, default=str)
+            os.replace(temp_file_path, self.status_file_path)
+
+            logging.debug(f"Successfully updated indicators status file with {len(results)} indicators.")
+
+        except Exception as e:
+            logging.error(f"Failed to fetch or save indicators: {e}", exc_info=True)
+
+    def run(self):
+        """Main loop to periodically compute and save indicators."""
+        logging.info("Starting Indicators Fetcher loop (update interval: 30s)")
+        while True:
+            try:
+                self.fetch_and_save_indicators()
+            except Exception as e:
+                logging.error(f"Indicators Fetcher loop error: {e}", exc_info=True)
+            time.sleep(30)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the Indicators Data Fetcher.")
+    parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
+    args = parser.parse_args()
+
+    fetcher = IndicatorsFetcher(log_level=args.log_level)
+    try:
+        fetcher.run()
+    except KeyboardInterrupt:
+        logging.info("Indicators Data Fetcher stopped.")

main_app.py

@@ -8,47 +8,42 @@ import multiprocessing
 import schedule
 import sqlite3
 import pandas as pd
-from datetime import datetime, timezone
+from datetime import datetime
 import importlib
+from dotenv import load_dotenv
+load_dotenv()
 # --- REMOVED: import signal ---
 # --- REMOVED: from queue import Empty ---
 
 from logging_utils import setup_logging
-# --- Using the new high-performance WebSocket utility for live prices ---
 from live_market_utils import start_live_feed
-# --- Import the base class for type hinting (optional but good practice) ---
 from strategies.base_strategy import BaseStrategy
+from dashboard import DashboardRenderer
+from rich.live import Live
+from hyperliquid.info import Info
+from hyperliquid.utils import constants
 
 # --- Configuration ---
-WATCHED_COINS = ["BTC", "ETH", "SOL", "BNB", "HYPE", "ASTER", "ZEC", "PUMP", "SUI", "xyz:BRENTOIL", "xyz:CL"]
+WATCHED_COINS = ["BTC", "ETH", "SOL", "BNB", "HYPE", "SUI", "xyz:BRENTOIL", "xyz:CL", "xyz:GOLD", "xyz:SILVER", "mkts:USTECH", "xyz:XYZ100"]
 # Display name mapping for dashboard (internal symbol -> display name)
 COIN_DISPLAY_NAMES = {
     "xyz:BRENTOIL": "BRENT",
-    "xyz:CL": "WTI"
+    "xyz:CL": "WTI",
+    "xyz:GOLD": "GOLD",
+    "xyz:SILVER": "SILVER",
+    "mkts:USTECH": "USTECH",
+    "xyz:XYZ100": "XYZ100"
 }
 LIVE_CANDLE_FETCHER_SCRIPT = "live_candle_fetcher.py"
 RESAMPLER_SCRIPT = "resampler.py" 
 # --- REMOVED: Market Cap Fetcher ---
 # --- REMOVED: trade_executor.py is no longer a script ---
 DASHBOARD_DATA_FETCHER_SCRIPT = "dashboard_data_fetcher.py"
+INDICATORS_FETCHER_SCRIPT = "indicators_fetcher.py"
 STRATEGY_CONFIG_FILE = os.path.join("_data", "strategies.json")
 DB_PATH = os.path.join("_data", "market_data.db")
 # --- REMOVED: Market Cap File ---
 LOGS_DIR = "_logs"
-TRADE_EXECUTOR_STATUS_FILE = os.path.join(LOGS_DIR, "trade_executor_status.json")
-
-
-def format_market_cap(mc_value):
-    """Formats a large number into a human-readable market cap string."""
-    if not isinstance(mc_value, (int, float)) or mc_value == 0:
-        return "N/A"
-    if mc_value >= 1_000_000_000_000:
-        return f"${mc_value / 1_000_000_000_000:.2f}T"
-    if mc_value >= 1_000_000_000:
-        return f"${mc_value / 1_000_000_000:.2f}B"
-    if mc_value >= 1_000_000:
-        return f"${mc_value / 1_000_000:.2f}M"
-    return f"${mc_value:,.2f}"
 
 
 def run_live_candle_fetcher():
@@ -348,17 +343,60 @@ def run_dashboard_data_fetcher():
             time.sleep(10)
 
 
+def run_indicators_fetcher():
+    """Target function to run the indicators_fetcher.py script."""
+
+    # --- GRACEFUL SHUTDOWN HANDLER ---
+    import signal
+
+    def handle_shutdown_signal(signum, frame):
+        try:
+            logging.info(f"Shutdown signal ({signum}) received. Initiating graceful exit...")
+        except NameError:
+            print(f"[IndicatorsFetcher] Shutdown signal ({signum}) received. Initiating graceful exit...")
+        raise KeyboardInterrupt
+
+    signal.signal(signal.SIGTERM, handle_shutdown_signal)
+    # --- END GRACEFUL SHUTDOWN HANDLER ---
+
+    log_file = os.path.join(LOGS_DIR, "indicators_fetcher.log")
+    while True:
+        try:
+            with open(log_file, 'a') as f:
+                f.write(f"\n--- Starting Indicators Fetcher at {datetime.now()} ---\n")
+                subprocess.run([sys.executable, INDICATORS_FETCHER_SCRIPT, "--log-level", "normal"], check=True, stdout=f, stderr=subprocess.STDOUT)
+        except KeyboardInterrupt:
+            logging.info("Indicators Fetcher stopping.")
+            break
+        except (subprocess.CalledProcessError, Exception) as e:
+            with open(log_file, 'a') as f:
+                f.write(f"\n--- PROCESS ERROR at {datetime.now()} ---\n")
+                f.write(f"Indicators Fetcher failed: {e}. Restarting...\n")
+            time.sleep(10)
+
+
 class MainApp:
     def __init__(self, coins_to_watch: list, processes: dict, strategy_configs: dict, shared_prices: dict):
         self.watched_coins = coins_to_watch
         self.shared_prices = shared_prices
         self.prices = {}
-        # --- REMOVED: self.market_caps ---
-        self.open_positions = {}
         self.background_processes = processes
         self.process_status = {}
         self.strategy_configs = strategy_configs
         self.strategy_statuses = {}
+        self.indicators_status = {}
+        self.account_data = None
+        self.wallet_address = os.environ.get("MAIN_WALLET_ADDRESS")
+        if self.wallet_address:
+            self.info_client = Info(constants.MAINNET_API_URL, skip_ws=True)
+        else:
+            self.info_client = None
+        self.renderer = DashboardRenderer(table_visibility={
+            "market": True,
+            "strategies": False,
+            "indicators": True,
+            "balances": True,
+        })
 
     def read_prices(self):
         """Reads the latest prices directly from the shared memory dictionary."""
@@ -386,190 +424,78 @@ class MainApp:
                     enabled_statuses[name] = {"current_signal": "Initializing..."}
         self.strategy_statuses = enabled_statuses
 
-    def read_executor_status(self):
-        """Reads the live status file from the trade executor."""
-        if os.path.exists(TRADE_EXECUTOR_STATUS_FILE):
+    def read_indicators_status(self):
+        """Reads the indicators status JSON file."""
+        status_file = os.path.join(LOGS_DIR, "indicators_status.json")
+        if os.path.exists(status_file):
             try:
-                with open(TRADE_EXECUTOR_STATUS_FILE, 'r', encoding='utf-8') as f:
-                    # --- FIX: Read the 'open_positions' key from the file ---
-                    status_data = json.load(f)
-                    self.open_positions = status_data.get('open_positions', {})
+                with open(status_file, 'r', encoding='utf-8') as f:
+                    self.indicators_status = json.load(f)
             except (IOError, json.JSONDecodeError):
-                logging.debug("Could not read trade executor status file.")
+                self.indicators_status = {}
         else:
-            self.open_positions = {}
+            self.indicators_status = {}
+
+    def read_account_data(self):
+        """Fetches account balances and positions from Hyperliquid API."""
+        if not self.wallet_address or not self.info_client:
+            self.account_data = None
+            return
+        try:
+            perp_state = self.info_client.user_state(self.wallet_address)
+            spot_state = self.info_client.spot_user_state(self.wallet_address)
+
+            margin_summary = perp_state.get('marginSummary', {})
+            account_value = float(margin_summary.get('accountValue', 0))
+            margin_used = float(margin_summary.get('totalMarginUsed', 0))
+            utilization = (margin_used / account_value) * 100 if account_value > 0 else 0
+
+            spot_balances = spot_state.get('balances', [])
+            positions = perp_state.get('assetPositions', [])
+
+            self.account_data = {
+                'account_value': account_value,
+                'margin_used': margin_used,
+                'utilization': utilization,
+                'spot_balances': spot_balances,
+                'positions': positions,
+            }
+
+        except Exception as e:
+            logging.error(f"Could not fetch account data: {e}")
+            self.account_data = None
 
     def check_process_status(self):
         """Checks if the background processes are still running."""
         for name, process in self.background_processes.items():
             self.process_status[name] = "Running" if process.is_alive() else "STOPPED"
 
-    def _format_price(self, price_val, width=10):
-        """Helper function to format prices for the dashboard."""
-        try:
-            price_float = float(price_val)
-            if price_float < 1:
-                price_str = f"{price_float:>{width}.6f}"
-            elif price_float < 100:
-                price_str = f"{price_float:>{width}.4f}"
-            else:
-                price_str = f"{price_float:>{width}.2f}"
-        except (ValueError, TypeError):
-            price_str = f"{'Loading...':>{width}}"
-        return price_str
+    def toggle_table(self, table_name, enabled=None):
+        """Toggle a dashboard table's visibility at runtime."""
+        return self.renderer.toggle_table(table_name, enabled)
 
     def display_dashboard(self):
-        """Displays a formatted dashboard with side-by-side tables."""
-        print("\x1b[H\x1b[J", end="") # Clear screen
-        
-        left_table_lines = ["--- Market Dashboard ---"]
-        # --- MODIFIED: Adjusted width for new columns ---
-        left_table_width = 65
-        left_table_lines.append("-" * left_table_width)
-        # --- MODIFIED: Replaced Market Cap with Gap ---
-        left_table_lines.append(f"{'#':<2} | {'Coin':^6} | {'Best Bid':>10} | {'Live Price':>10} | {'Best Ask':>10} | {'Gap':>10} |")
-        left_table_lines.append("-" * left_table_width)
-        for i, coin in enumerate(self.watched_coins, 1):
-            # Use display name for dashboard, but keep internal symbol for price lookup
-            display_name = COIN_DISPLAY_NAMES.get(coin, coin)
-            
-            # --- MODIFIED: Fetch all three price types ---
-            mid_price = self.prices.get(coin, "Loading...")
-            bid_price = self.prices.get(f"{coin}_bid", "Loading...")
-            ask_price = self.prices.get(f"{coin}_ask", "Loading...")
-
-            # --- MODIFIED: Use the new formatting helper ---
-            formatted_mid = self._format_price(mid_price)
-            formatted_bid = self._format_price(bid_price)
-            formatted_ask = self._format_price(ask_price)
-
-            # --- MODIFIED: Calculate gap ---
-            gap_str = f"{'Loading...':>10}"
-            try:
-                # Calculate the spread
-                gap_val = float(ask_price) - float(bid_price)
-                # Format gap with high precision, similar to price
-                if gap_val < 1:
-                    gap_str = f"{gap_val:>{10}.6f}"
-                else:
-                    gap_str = f"{gap_val:>{10}.4f}"
-            except (ValueError, TypeError):
-                pass # Keep 'Loading...'
-
-            # --- REMOVED: Market Cap logic ---
-            
-            # --- MODIFIED: Print all price columns including gap ---
-            left_table_lines.append(f"{i:<2} | {display_name:^6} | {formatted_bid} | {formatted_mid} | {formatted_ask} | {gap_str} |")
-        left_table_lines.append("-" * left_table_width)
-
-        right_table_lines = ["--- Strategy Status ---"]
-        # --- FIX: Adjusted table width after removing parameters ---
-        right_table_width = 105
-        right_table_lines.append("-" * right_table_width)
-        # --- FIX: Removed 'Parameters' from header ---
-        right_table_lines.append(f"{'#':^2} | {'Strategy Name':<25} | {'Coin':^6} | {'Signal':^8} | {'Signal Price':>12} | {'Last Change':>17} | {'TF':^5} | {'Size':^8} |")
-        right_table_lines.append("-" * right_table_width)
-        for i, (name, status) in enumerate(self.strategy_statuses.items(), 1):
-            signal = status.get('current_signal', 'N/A')
-            price = status.get('signal_price')
-            price_display = f"{price:.4f}" if isinstance(price, (int, float)) else "-"
-            last_change = status.get('last_signal_change_utc')
-            last_change_display = 'Never'
-            if last_change:
-                dt_utc = datetime.fromisoformat(last_change.replace('Z', '+00:00')).replace(tzinfo=timezone.utc)
-                dt_local = dt_utc.astimezone(None)
-                last_change_display = dt_local.strftime('%Y-%m-%d %H:%M')
-            
-            config_params = self.strategy_configs.get(name, {}).get('parameters', {})
-            
-            # --- FIX: Read coin/size from status file first, fallback to config ---
-            coin = status.get('coin', config_params.get('coin', 'N/A'))
-            
-            # --- FIX: Handle nested 'coins_to_copy' logic for size ---
-            # --- MODIFIED: Read 'size' from status first, then config, then 'Multi' ---
-            size = status.get('size')
-            if not size:
-                if 'coins_to_copy' in config_params:
-                    size = 'Multi'
-                else:
-                    size = config_params.get('size', 'N/A')
-            
-            timeframe = config_params.get('timeframe', 'N/A')
-            
-            # --- FIX: Removed parameter string logic ---
-            
-            # --- FIX: Removed 'params_str' from the formatted line ---
-                
-            size_display = f"{size:>8}"
-            if isinstance(size, (int, float)):
-                # --- MODIFIED: More flexible size formatting ---
-                if size < 0.0001:
-                    size_display = f"{size:>8.6f}"
-                elif size < 1:
-                    size_display = f"{size:>8.4f}"
-                else:
-                    size_display = f"{size:>8.2f}"
-            # --- END NEW LOGIC ---
-            
-            right_table_lines.append(f"{i:^2} | {name:<25} | {coin:^6} | {signal:^8} | {price_display:>12} | {last_change_display:>17} | {timeframe:^5} | {size_display} |")
-        right_table_lines.append("-" * right_table_width)
-        
-        output_lines = []
-        max_rows = max(len(left_table_lines), len(right_table_lines))
-        separator = "   "
-        indent = " " * 10
-        for i in range(max_rows):
-            left_part = left_table_lines[i] if i < len(left_table_lines) else " " * left_table_width
-            right_part = indent + right_table_lines[i] if i < len(right_table_lines) else ""
-            output_lines.append(f"{left_part}{separator}{right_part}")
-        
-        output_lines.append("\n--- Open Positions ---")
-        pos_table_width = 100
-        output_lines.append("-" * pos_table_width)
-        output_lines.append(f"{'Account':<10} | {'Coin':<6} | {'Size':>15} | {'Entry Price':>12} | {'Mark Price':>12} | {'PNL':>15} | {'Leverage':>10} |")
-        output_lines.append("-" * pos_table_width)
-
-        # --- FIX: Correctly read and display open positions ---
-        if not self.open_positions:
-            output_lines.append(f"{'No open positions.':^{pos_table_width}}")
-        else:
-            for account, positions in self.open_positions.items():
-                if not positions:
-                    continue
-                for coin, pos in positions.items():
-                    try:
-                        size_f = float(pos.get('size', 0))
-                        entry_f = float(pos.get('entry_price', 0))
-                        mark_f = float(self.prices.get(coin, 0))
-                        pnl_f = (mark_f - entry_f) * size_f if size_f > 0 else (entry_f - mark_f) * abs(size_f)
-                        lev = pos.get('leverage', 1)
-                        
-                        size_str = f"{size_f:>{15}.5f}"
-                        entry_str = f"{entry_f:>{12}.2f}"
-                        mark_str = f"{mark_f:>{12}.2f}"
-                        pnl_str = f"{pnl_f:>{15}.2f}"
-                        lev_str = f"{lev}x"
-                        
-                        output_lines.append(f"{account:<10} | {coin:<6} | {size_str} | {entry_str} | {mark_str} | {pnl_str} | {lev_str:>10} |")
-                    except (ValueError, TypeError):
-                         output_lines.append(f"{account:<10} | {coin:<6} | {'Error parsing data...':^{pos_table_width-20}} |")
-
-        output_lines.append("-" * pos_table_width)
-            
-        final_output = "\n".join(output_lines)
-        print(final_output)
-        sys.stdout.flush()
+        """Build and return the rich dashboard layout."""
+        return self.renderer.build_layout(
+            self.watched_coins,
+            self.prices,
+            COIN_DISPLAY_NAMES,
+            self.strategy_statuses,
+            self.strategy_configs,
+            self.indicators_status,
+            self.account_data
+        )
 
     def run(self):
         """Main loop to read data, display dashboard, and check processes."""
-        while True:
-            self.read_prices()
-            # --- REMOVED: self.read_market_caps() ---
-            self.read_strategy_statuses()
-            self.read_executor_status()
-            # --- REMOVED: self.check_process_status() ---
-            self.display_dashboard()
-            time.sleep(0.5)
+        with Live(self.display_dashboard(), refresh_per_second=2, console=self.renderer.console) as live:
+            while True:
+                self.read_prices()
+                self.read_strategy_statuses()
+                self.read_indicators_status()
+                self.read_account_data()
+                live.update(self.display_dashboard())
+                time.sleep(0.5)
 
 if __name__ == "__main__":
     setup_logging('normal', 'MainApp')
@@ -613,6 +539,7 @@ if __name__ == "__main__":
         processes["Resampler"] = multiprocessing.Process(target=resampler_scheduler, args=(list(required_timeframes),), daemon=True)
         # --- REMOVED: Market Cap Fetcher Process ---
         processes["Dashboard Data"] = multiprocessing.Process(target=run_dashboard_data_fetcher, daemon=True)
+        processes["Indicators"] = multiprocessing.Process(target=run_indicators_fetcher, daemon=True)
 
         processes["Position Manager"] = multiprocessing.Process(
             target=run_position_manager, 

position_monitor.py

@@ -77,14 +77,13 @@ class PositionMonitor:
             output_lines.append("\n--- Perpetuals Account Summary ---")
             output_lines.append(f"  Account Value: ${account_value:,.2f}  |  Margin Used: ${margin_used:,.2f}  |  Utilization: {utilization:.2f}%")
             
-            # --- 2. Spot Balances Summary ---
+            # --- 2. Spot Balances Table ---
             output_lines.append("\n--- Spot Balances ---")
             spot_balances = spot_state.get('balances', [])
             if not spot_balances:
                 output_lines.append("  No spot balances found.")
             else:
-                balances_str = ", ".join([f"{b.get('coin')}: {float(b.get('total', 0)):,.4f}" for b in spot_balances if float(b.get('total', 0)) > 0])
-                output_lines.append(f"  {balances_str}")
+                self.build_spot_balances_table(spot_balances, output_lines)
                 
             # --- 3. Open Positions Table ---
             output_lines.append("\n--- Open Perpetual Positions ---")
@@ -106,6 +105,23 @@ class PositionMonitor:
         self._lines_printed = len(output_lines)
         sys.stdout.flush()
 
+    def build_spot_balances_table(self, spot_balances: list, output_lines: list):
+        """Builds the text for the spot balances table."""
+        header = f"| {'Coin':<10} | {'Total':>18} |"
+        output_lines.append(header)
+        output_lines.append("-" * len(header))
+        
+        for balance in spot_balances:
+            coin = balance.get('coin', 'Unknown')
+            total = float(balance.get('total', 0))
+            
+            coin_str = f"{coin:<10}"
+            total_str = f"{total:>18,.4f}"
+            
+            output_lines.append(f"| {coin_str} | {total_str} |")
+        
+        output_lines.append("-" * len(header))
+
     def build_positions_table(self, positions: list, coin_to_strategy_map: dict, output_lines: list):
         """Builds the text for the positions summary table."""
         header = f"| {'Strategy':<25} | {'Coin':<6} | {'Side':<5} | {'Size':>15} | {'Entry Price':>12} | {'Mark Price':>12} | {'PNL':>15} | {'Leverage':>10} |"

requirements.txt

@@ -39,6 +39,7 @@ pydantic_core==2.41.5
 python-dateutil==2.9.0.post0
 python-dotenv==1.2.1
 pytz==2025.2
+rich==13.9.4
 regex==2025.11.3
 requests==2.32.5
 rlp==4.1.0