feat: add 5 dashboard features — dark mode, trade history, backtests, model insights, alerts
- Dark mode: class-based theme toggle with localStorage persistence and flash prevention - Trade History (/trades): paginated table, stats cards, equity curve chart with DB API endpoints - Backtest Viewer (/backtests): log parser for 35 backtest results, sidebar + detail + comparison tabs - Model Insights: dashboard card + dialog showing feature importance, regime distribution, training history - Alert/Signal Log (/alerts): signal stats, filterable table with execution tracking - API: 8 new endpoints with psycopg2 DB connection pool - Dark mode sweep across books page, about dialog, and all dashboard components - Architecture docs rewritten with Mermaid diagrams (23 docs) - README and FEATURES.md rewritten bilingual (Indonesian + English) - main_live.py: write model_metrics.json on startup and retrain Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
GifariKemal
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
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commit
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@@ -2,15 +2,43 @@
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> **File:** `src/trade_logger.py`
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> **Class:** `TradeLogger`
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> **Storage:** PostgreSQL (primary) + CSV (fallback)
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> **Storage:** PostgreSQL (primary) + CSV (*fallback*)
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---
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## Apa Itu Trade Logger?
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## Apa Itu *Trade Logger*?
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Trade Logger mencatat **setiap trade, sinyal, dan kondisi pasar** secara otomatis ke database dan file CSV. Data ini digunakan untuk analisis performa, retraining ML model, dan debugging.
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*Trade Logger* mencatat **setiap trade, sinyal, dan kondisi pasar** secara otomatis ke database dan file CSV. Data ini digunakan untuk analisis performa, retraining ML model, dan debugging.
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**Analogi:** Trade Logger seperti **black box di pesawat** — merekam semua yang terjadi untuk analisis setelah penerbangan (trading).
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**Analogi:** *Trade Logger* seperti ***black box* di pesawat** — merekam semua yang terjadi untuk analisis setelah penerbangan (trading).
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---
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## Alur *Dual Storage*
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```mermaid
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flowchart TD
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A["Event Terjadi\n(trade / signal / snapshot)"] --> B[TradeLogger]
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B --> C{DB tersedia?}
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C -- Ya --> D["PostgreSQL\n(Primary)"]
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C -- Ya --> E["CSV\n(Backup)"]
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C -- Tidak --> E
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D --> F["trades table\nsignals table\nmarket_snapshots\nbot_status"]
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E --> G["data/trade_logs/\ntrades/ | signals/ | snapshots/\n(file bulanan YYYY_MM.csv)"]
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style A fill:#2d333b,stroke:#adbac7,color:#adbac7
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style B fill:#1f6feb,stroke:#58a6ff,color:#fff
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style C fill:#3d444d,stroke:#adbac7,color:#adbac7
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style D fill:#238636,stroke:#3fb950,color:#fff
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style E fill:#9e6a03,stroke:#d29922,color:#fff
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style F fill:#238636,stroke:#3fb950,color:#fff
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style G fill:#9e6a03,stroke:#d29922,color:#fff
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```
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**Prinsip *dual storage*:**
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- **DB tersedia?** — Tulis ke PostgreSQL **DAN** CSV (double safety)
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- **DB tidak tersedia?** — CSV saja (*graceful degradation*)
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- CSV **selalu** ditulis sebagai *fallback*, tidak peduli status DB
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---
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@@ -27,14 +55,14 @@ Setiap trade dibuka/ditutup dicatat lengkap:
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| **Hasil** | profit_usd, profit_pips, duration_seconds |
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| **Waktu** | open_time, close_time |
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| **Market** | regime, volatility, session, spread, ATR |
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| **SMC** | signal, confidence, reason, FVG/OB/BOS/CHoCH flags |
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| **ML** | signal, confidence |
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| **SMC** | *signal*, confidence, reason, FVG/OB/BOS/CHoCH flags |
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| **ML** | *signal*, confidence |
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| **Dynamic** | market_quality, market_score, threshold |
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| **Exit** | exit_reason, exit_regime, exit_ml_signal |
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| **Balance** | balance_before, balance_after, equity_at_entry |
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| **Features** | JSON snapshot fitur saat entry & exit |
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| **Features** | JSON *snapshot* fitur saat entry & exit |
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### 2. Signal Record (Per Sinyal)
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### 2. *Signal* Record (Per Sinyal)
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Setiap sinyal yang dihasilkan (termasuk yang **tidak** dieksekusi):
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@@ -47,9 +75,9 @@ trade_executed (bool)
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execution_reason ("executed" / "below_threshold" / "max_positions" / ...)
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```
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### 3. Market Snapshot (Periodik)
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### 3. Market *Snapshot* (Periodik)
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Snapshot kondisi pasar secara berkala:
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*Snapshot* kondisi pasar secara berkala:
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```
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timestamp, symbol, price, OHLC
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@@ -62,56 +90,38 @@ features (JSON)
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---
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## Dual Storage
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## *Dual Storage*
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```
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Event Terjadi (trade/signal/snapshot)
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v
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┌─────────────────────┐ ┌──────────────────┐
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│ PostgreSQL (Primary) │ │ CSV (Fallback) │
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│ │ │ │
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│ ├── trades table │ │ data/trade_logs/│
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│ ├── signals table │ │ ├── trades/ │
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│ ├── market_snapshots │ │ │ └── trades_2025_02.csv
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│ └── bot_status │ │ ├── signals/ │
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│ │ │ │ └── signals_2025_02.csv
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│ Cepat, queryable, │ │ └── snapshots/ │
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│ thread-safe pooling │ │ └── snapshots_2025_02.csv
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└─────────────────────┘ │ │
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│ Selalu ditulis │
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│ (backup) │
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└──────────────────┘
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```mermaid
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flowchart TD
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EV["Event Terjadi<br/>(trade / signal / snapshot)"] --> PG["PostgreSQL (Primary)<br/>trades, signals,<br/>market_snapshots, bot_status<br/>Cepat, queryable, thread-safe pooling"]
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EV --> CSV["CSV (Fallback)<br/>data/trade_logs/<br/>trades/, signals/, snapshots/<br/>Selalu ditulis (backup)"]
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```
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- **DB tidak tersedia?** → CSV saja (graceful degradation)
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- **DB tidak tersedia?** → CSV saja (*graceful degradation*)
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- **DB tersedia?** → Tulis ke DB **DAN** CSV (double safety)
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---
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## Proses Log Trade
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```mermaid
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flowchart TD
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OPEN["Trade Dibuka"] --> LOG_OPEN["log_trade_open()<br/>ticket, entry_price, regime, smc, ml"]
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LOG_OPEN --> MEM["Simpan ke _pending_trades di memory"]
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LOG_OPEN --> DB_INS["INSERT ke database (trades table)"]
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MEM --> WAIT["... trading berjalan ..."]
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DB_INS --> WAIT
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WAIT --> CLOSE["Trade Ditutup"]
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CLOSE --> LOG_CLOSE["log_trade_close()<br/>ticket, exit_price, profit, exit_reason"]
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LOG_CLOSE --> FETCH["Ambil data pending dari memory"]
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LOG_CLOSE --> DUR["Hitung durasi: close - open"]
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FETCH --> UPD["UPDATE database<br/>(exit_price, profit, duration)"]
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DUR --> UPD
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UPD --> CSV["APPEND ke CSV<br/>(trades_YYYY_MM.csv)"]
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```
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Trade Dibuka:
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v
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log_trade_open(ticket, entry_price, regime, smc_*, ml_*, ...)
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├── Simpan ke _pending_trades[ticket] (di memory)
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└── INSERT ke database (trades table)
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... trading berjalan ...
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Trade Ditutup:
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v
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log_trade_close(ticket, exit_price, profit, exit_reason, ...)
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├── Ambil data pending dari memory
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├── Hitung durasi: close_time - open_time
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├── UPDATE database (exit_price, profit, duration, ...)
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└── APPEND ke CSV (trades_YYYY_MM.csv)
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```
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Data *pending* disimpan dalam dictionary `_pending_trades[ticket]` selama trade masih terbuka. Ketika trade ditutup, data entry digabung dengan data exit menjadi satu `TradeRecord` lengkap sebelum ditulis ke CSV.
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---
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@@ -125,9 +135,11 @@ log_trade_close(ticket, exit_price, profit, exit_reason, ...)
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| `get_trades_for_training(30)` | Data untuk ML retraining |
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| `get_stats()` | Statistik logger |
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Setiap helper method mencoba query dari PostgreSQL terlebih dahulu. Jika DB tidak tersedia, otomatis *fallback* ke pembacaan file CSV — konsisten dengan prinsip *graceful degradation*.
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---
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## Thread Safety
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## *Thread Safety*
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```python
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self._lock = threading.Lock()
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@@ -137,6 +149,8 @@ with self._lock:
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# Write to CSV
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```
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Semua operasi tulis ke file CSV dilindungi oleh `threading.Lock()` untuk menjamin *thread safety*. Ini mencegah korupsi data ketika multiple thread mencoba menulis ke file yang sama secara bersamaan (misalnya log trade close dan log *signal* terjadi hampir bersamaan).
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---
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## File CSV (Terorganisir per Bulan)
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