diff --git a/VWAPBB_README.md b/VWAPBB_README.md new file mode 100644 index 000000000000..b0acb89b8380 --- /dev/null +++ b/VWAPBB_README.md @@ -0,0 +1,74 @@ +# VWAPBB Strategy – XAUUSD + +A full-stack trading strategy application implementing the VWAP + Bollinger Bands (VWAPBB) strategy for XAUUSD on the 5-minute timeframe. + +## Strategy Overview + +### Indicators +- **Bollinger Bands (BB)** – 20-period, 2 standard deviations on the 5-minute chart +- **EMA 200** – Calculated on the 15-minute timeframe for trend filtering +- **VWAP** – Volume Weighted Average Price, resetting daily + +### Trend Filter +The EMA 200 on the 15-minute timeframe determines the overall market trend: +- **Bullish**: Price above EMA 200 → only BUY signals +- **Bearish**: Price below EMA 200 → only SELL signals + +### Buy Conditions (Bullish Trend) + +**Scenario 1**: Price above EMA 200 AND above VWAP +- Price touches/approaches VWAP or Lower Bollinger Band +- Bullish engulfing pattern appears → Enter at candle close + +**Scenario 2**: Price above EMA 200 but below VWAP +- Price touches/approaches Lower Bollinger Band +- Bullish engulfing pattern appears → Enter at candle close + +### Sell Conditions (Bearish Trend) +- Price below EMA 200 +- Price touches/approaches VWAP or Upper Bollinger Band +- Bearish engulfing pattern appears → Enter at candle close + +## Tech Stack + +### Backend +- **Python 3.12** + **FastAPI** +- **pandas** / **numpy** for indicator calculations +- Simulated XAUUSD data generation + +### Frontend +- **React 18** + **TypeScript** + **Vite** +- **Lightweight Charts** (TradingView) for interactive charting +- Dark theme with responsive design + +## Getting Started + +### Prerequisites +- Python 3.10+ +- Node.js 18+ + +### Backend Setup +```bash +cd backend +python3 -m venv venv +source venv/bin/activate +pip install -r requirements.txt +python main.py +``` +The API will be available at `http://localhost:8000`. + +### Frontend Setup +```bash +cd frontend +npm install +npm run dev +``` +The dashboard will be available at `http://localhost:5173`. + +## API Endpoints + +| Method | Endpoint | Description | +|--------|----------|-------------| +| GET | `/api/strategy?num_candles=500` | Get candles, indicators, and signals | +| GET | `/api/health` | Health check | +| GET | `/docs` | Interactive API documentation (Swagger) | diff --git a/backend/data_provider.py b/backend/data_provider.py new file mode 100644 index 000000000000..cbf009607ab7 --- /dev/null +++ b/backend/data_provider.py @@ -0,0 +1,73 @@ +import time +import numpy as np +import pandas as pd + + +def generate_simulated_xauusd( + num_candles: int = 500, + timeframe_seconds: int = 300, + base_price: float = 2650.0, +) -> pd.DataFrame: + """ + Generate simulated XAUUSD OHLCV data for demonstration. + Produces realistic gold price movements with trends and volatility. + """ + np.random.seed(42) + + end_time = int(time.time()) + end_time = end_time - (end_time % timeframe_seconds) + timestamps = [end_time - (num_candles - 1 - i) * timeframe_seconds for i in range(num_candles)] + + prices = [base_price] + volatility = 0.0008 # ~0.08% per 5-min candle for gold + + # Add a slow trend component + trend = np.sin(np.linspace(0, 4 * np.pi, num_candles)) * 15 + + for i in range(1, num_candles): + change = np.random.normal(0, volatility) * prices[-1] + trend_component = (trend[i] - trend[i - 1]) + new_price = prices[-1] + change + trend_component + prices.append(max(new_price, base_price * 0.95)) + + candles = [] + for i in range(num_candles): + p = prices[i] + spread = p * np.random.uniform(0.0002, 0.001) + + o = p + np.random.normal(0, spread * 0.3) + c = p + np.random.normal(0, spread * 0.3) + + h = max(o, c) + abs(np.random.normal(0, spread * 0.5)) + l = min(o, c) - abs(np.random.normal(0, spread * 0.5)) + + vol = np.random.uniform(100, 5000) + + candles.append({ + "timestamp": timestamps[i], + "open": round(o, 2), + "high": round(h, 2), + "low": round(l, 2), + "close": round(c, 2), + "volume": round(vol, 2), + }) + + return pd.DataFrame(candles) + + +def resample_to_15m(df_5m: pd.DataFrame) -> pd.DataFrame: + """Resample 5-minute candles to 15-minute candles.""" + df = df_5m.copy() + df["datetime"] = pd.to_datetime(df["timestamp"], unit="s") + df = df.set_index("datetime") + + resampled = df.resample("15min").agg({ + "timestamp": "first", + "open": "first", + "high": "max", + "low": "min", + "close": "last", + "volume": "sum", + }).dropna() + + return resampled.reset_index(drop=True) diff --git a/backend/indicators.py b/backend/indicators.py new file mode 100644 index 000000000000..2a0166f2f70d --- /dev/null +++ b/backend/indicators.py @@ -0,0 +1,74 @@ +import numpy as np +import pandas as pd + + +def calculate_ema(series: pd.Series, period: int) -> pd.Series: + """Calculate Exponential Moving Average.""" + return series.ewm(span=period, adjust=False).mean() + + +def calculate_bollinger_bands( + series: pd.Series, period: int = 20, std_dev: float = 2.0 +) -> tuple[pd.Series, pd.Series, pd.Series]: + """Calculate Bollinger Bands (upper, middle, lower).""" + middle = series.rolling(window=period).mean() + rolling_std = series.rolling(window=period).std() + upper = middle + (rolling_std * std_dev) + lower = middle - (rolling_std * std_dev) + return upper, middle, lower + + +def calculate_vwap(df: pd.DataFrame) -> pd.Series: + """ + Calculate Volume Weighted Average Price. + Resets at the start of each trading day. + """ + df = df.copy() + df["typical_price"] = (df["high"] + df["low"] + df["close"]) / 3.0 + df["tp_volume"] = df["typical_price"] * df["volume"] + + df["date"] = pd.to_datetime(df["timestamp"], unit="s").dt.date + + vwap_values = [] + for _, group in df.groupby("date"): + cum_tp_vol = group["tp_volume"].cumsum() + cum_vol = group["volume"].cumsum() + vwap = cum_tp_vol / cum_vol.replace(0, np.nan) + vwap_values.append(vwap) + + if vwap_values: + return pd.concat(vwap_values).sort_index() + return pd.Series(dtype=float) + + +def is_bullish_engulfing( + prev_open: float, + prev_close: float, + curr_open: float, + curr_close: float, +) -> bool: + """Detect bullish engulfing candlestick pattern.""" + prev_bearish = prev_close < prev_open + curr_bullish = curr_close > curr_open + engulfs = curr_open <= prev_close and curr_close >= prev_open + return prev_bearish and curr_bullish and engulfs + + +def is_bearish_engulfing( + prev_open: float, + prev_close: float, + curr_open: float, + curr_close: float, +) -> bool: + """Detect bearish engulfing candlestick pattern.""" + prev_bullish = prev_close > prev_open + curr_bearish = curr_close < curr_open + engulfs = curr_open >= prev_close and curr_close <= prev_open + return prev_bullish and curr_bearish and engulfs + + +def is_near(price: float, level: float, threshold_pct: float = 0.001) -> bool: + """Check if price is near a given level within a percentage threshold.""" + if level == 0: + return False + return abs(price - level) / level <= threshold_pct diff --git a/backend/main.py b/backend/main.py new file mode 100644 index 000000000000..884a9be84b24 --- /dev/null +++ b/backend/main.py @@ -0,0 +1,92 @@ +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware + +from models import Candle, IndicatorValues, StrategyResponse, Trend +from indicators import calculate_ema, calculate_bollinger_bands, calculate_vwap +from strategy import determine_trend, generate_signals +from data_provider import generate_simulated_xauusd, resample_to_15m + +app = FastAPI( + title="VWAPBB Strategy – XAUUSD", + description="Full-stack VWAP + Bollinger Bands trading strategy for XAUUSD", + version="1.0.0", +) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.get("/api/strategy", response_model=StrategyResponse) +def get_strategy_data(num_candles: int = 500): + """ + Generate XAUUSD data with all indicators and trading signals. + Returns candles, indicator overlays, and buy/sell signals. + """ + # Generate 5-minute candle data + df_5m = generate_simulated_xauusd(num_candles=num_candles) + + # Resample to 15-minute for EMA 200 trend filter + df_15m = resample_to_15m(df_5m) + ema_200_15m_series = calculate_ema(df_15m["close"], 200) + ema_200_15m_value = float(ema_200_15m_series.iloc[-1]) if len(ema_200_15m_series) > 0 else None + + # Calculate indicators on 5-minute data + bb_upper, bb_middle, bb_lower = calculate_bollinger_bands(df_5m["close"]) + vwap = calculate_vwap(df_5m) + + # Determine current trend + current_price = float(df_5m["close"].iloc[-1]) + trend = determine_trend(ema_200_15m_value or 0, current_price) + + # Generate signals + signals = generate_signals(df_5m, ema_200_15m_value or 0) + + # Build response + candles = [ + Candle( + timestamp=int(row["timestamp"]), + open=row["open"], + high=row["high"], + low=row["low"], + close=row["close"], + volume=row["volume"], + ) + for _, row in df_5m.iterrows() + ] + + indicators = [] + for i, row in df_5m.iterrows(): + idx = int(i) if not isinstance(i, int) else i + indicators.append( + IndicatorValues( + timestamp=int(row["timestamp"]), + ema_200=ema_200_15m_value, + vwap=float(vwap.iloc[idx]) if idx < len(vwap) and not vwap.isna().iloc[idx] else None, + bb_upper=float(bb_upper.iloc[idx]) if idx < len(bb_upper) and not bb_upper.isna().iloc[idx] else None, + bb_middle=float(bb_middle.iloc[idx]) if idx < len(bb_middle) and not bb_middle.isna().iloc[idx] else None, + bb_lower=float(bb_lower.iloc[idx]) if idx < len(bb_lower) and not bb_lower.isna().iloc[idx] else None, + ) + ) + + return StrategyResponse( + candles=candles, + indicators=indicators, + signals=signals, + trend=trend, + ema_200_15m=ema_200_15m_value, + ) + + +@app.get("/api/health") +def health_check(): + return {"status": "ok", "strategy": "VWAPBB", "pair": "XAUUSD"} + + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) diff --git a/backend/models.py b/backend/models.py new file mode 100644 index 000000000000..9e98c6d873c9 --- /dev/null +++ b/backend/models.py @@ -0,0 +1,48 @@ +from pydantic import BaseModel +from typing import Optional +from enum import Enum + + +class Trend(str, Enum): + BULLISH = "bullish" + BEARISH = "bearish" + NEUTRAL = "neutral" + + +class SignalType(str, Enum): + BUY = "buy" + SELL = "sell" + + +class Candle(BaseModel): + timestamp: int + open: float + high: float + low: float + close: float + volume: float + + +class IndicatorValues(BaseModel): + timestamp: int + ema_200: Optional[float] = None + vwap: Optional[float] = None + bb_upper: Optional[float] = None + bb_middle: Optional[float] = None + bb_lower: Optional[float] = None + + +class Signal(BaseModel): + timestamp: int + signal_type: SignalType + price: float + scenario: str + reason: str + + +class StrategyResponse(BaseModel): + candles: list[Candle] + indicators: list[IndicatorValues] + signals: list[Signal] + trend: Trend + ema_200_15m: Optional[float] = None diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 000000000000..72ec7aadd935 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,8 @@ +fastapi==0.115.6 +uvicorn==0.34.0 +pandas==2.2.3 +numpy==2.2.1 +pydantic==2.10.4 +httpx==0.28.1 +python-dotenv==1.0.1 +websockets==14.1 diff --git a/backend/strategy.py b/backend/strategy.py new file mode 100644 index 000000000000..e476f4c85d74 --- /dev/null +++ b/backend/strategy.py @@ -0,0 +1,136 @@ +import pandas as pd + +from models import Trend, SignalType, Signal +from indicators import ( + calculate_ema, + calculate_bollinger_bands, + calculate_vwap, + is_bullish_engulfing, + is_bearish_engulfing, + is_near, +) + + +NEAR_THRESHOLD = 0.0015 # 0.15% proximity threshold for XAUUSD + + +def determine_trend(ema_200_value: float, current_price: float) -> Trend: + """Determine trend based on price relative to EMA 200 from 15m timeframe.""" + if pd.isna(ema_200_value): + return Trend.NEUTRAL + if current_price > ema_200_value: + return Trend.BULLISH + elif current_price < ema_200_value: + return Trend.BEARISH + return Trend.NEUTRAL + + +def generate_signals( + df_5m: pd.DataFrame, + ema_200_15m_value: float, +) -> list[Signal]: + """ + Generate buy/sell signals based on the VWAPBB strategy. + + Parameters: + df_5m: DataFrame with 5-minute OHLCV candles + ema_200_15m_value: Current EMA 200 value from 15-minute timeframe + """ + if len(df_5m) < 21: + return [] + + df = df_5m.copy() + + # Calculate indicators on 5m data + bb_upper, bb_middle, bb_lower = calculate_bollinger_bands(df["close"]) + vwap = calculate_vwap(df) + + df["bb_upper"] = bb_upper + df["bb_middle"] = bb_middle + df["bb_lower"] = bb_lower + df["vwap"] = vwap + + signals: list[Signal] = [] + + for i in range(1, len(df)): + row = df.iloc[i] + prev = df.iloc[i - 1] + + price = row["close"] + trend = determine_trend(ema_200_15m_value, price) + + if trend == Trend.NEUTRAL: + continue + + if pd.isna(row["bb_lower"]) or pd.isna(row["bb_upper"]) or pd.isna(row["vwap"]): + continue + + # --- BUY CONDITIONS (Bullish Trend) --- + if trend == Trend.BULLISH: + if not is_bullish_engulfing( + prev["open"], prev["close"], row["open"], row["close"] + ): + continue + + # Scenario 1: Price above VWAP, touches VWAP or lower BB + if price > row["vwap"]: + near_vwap = is_near(row["low"], row["vwap"], NEAR_THRESHOLD) + near_lower_bb = is_near(row["low"], row["bb_lower"], NEAR_THRESHOLD) + if near_vwap or near_lower_bb: + trigger = "VWAP" if near_vwap else "Lower BB" + signals.append( + Signal( + timestamp=int(row["timestamp"]), + signal_type=SignalType.BUY, + price=price, + scenario="Buy Scenario 1", + reason=( + f"Bullish engulfing above EMA200 & VWAP. " + f"Price touched {trigger}." + ), + ) + ) + + # Scenario 2: Price below VWAP but above EMA200, touches lower BB + else: + near_lower_bb = is_near(row["low"], row["bb_lower"], NEAR_THRESHOLD) + if near_lower_bb: + signals.append( + Signal( + timestamp=int(row["timestamp"]), + signal_type=SignalType.BUY, + price=price, + scenario="Buy Scenario 2", + reason=( + "Bullish engulfing above EMA200, below VWAP. " + "Price touched Lower BB." + ), + ) + ) + + # --- SELL CONDITIONS (Bearish Trend) --- + elif trend == Trend.BEARISH: + if not is_bearish_engulfing( + prev["open"], prev["close"], row["open"], row["close"] + ): + continue + + near_vwap = is_near(row["high"], row["vwap"], NEAR_THRESHOLD) + near_upper_bb = is_near(row["high"], row["bb_upper"], NEAR_THRESHOLD) + + if near_vwap or near_upper_bb: + trigger = "VWAP" if near_vwap else "Upper BB" + signals.append( + Signal( + timestamp=int(row["timestamp"]), + signal_type=SignalType.SELL, + price=price, + scenario="Sell Scenario", + reason=( + f"Bearish engulfing below EMA200. " + f"Price touched {trigger}." + ), + ) + ) + + return signals diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 000000000000..ef614d25c11d --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,22 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + globals: globals.browser, + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 000000000000..4b1ee11576fa --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,13 @@ + + + + + + + VWAPBB Strategy – XAUUSD + + +
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&& vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "axios": "^1.16.1", + "lightweight-charts": "^5.2.0", + "react": "^19.2.6", + "react-dom": "^19.2.6" + }, + "devDependencies": { + "@eslint/js": "^10.0.1", + "@types/node": "^24.12.3", + "@types/react": "^19.2.14", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^6.0.1", + "eslint": "^10.3.0", + "eslint-plugin-react-hooks": "^7.1.1", + "eslint-plugin-react-refresh": "^0.5.2", + "globals": "^17.6.0", + "typescript": "~6.0.2", + "typescript-eslint": "^8.59.2", + "vite": "^8.0.12" + } +} diff --git a/frontend/src/App.css b/frontend/src/App.css new file mode 100644 index 000000000000..ac83bc898ab6 --- /dev/null +++ b/frontend/src/App.css @@ -0,0 +1,181 @@ +.app { + min-height: 100vh; + background: #0d1117; + color: #c9d1d9; + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; + padding: 20px; +} + +/* Header */ +.header { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 20px; + flex-wrap: wrap; + gap: 12px; +} + +.header-left { + display: flex; + align-items: center; + gap: 12px; +} + +.header-right { + display: flex; + align-items: center; + gap: 8px; +} + +.title { + font-size: 24px; + font-weight: 700; + margin: 0; + color: #f0f6fc; +} + +.pair-badge { + background: rgba(255, 152, 0, 0.15); + color: #ff9800; + padding: 4px 12px; + border-radius: 20px; + font-size: 13px; + font-weight: 600; +} + +.timeframe-badge { + background: rgba(33, 150, 243, 0.15); + color: #2196f3; + padding: 4px 12px; + border-radius: 20px; + font-size: 13px; + font-weight: 600; +} + +.candle-select { + background: #161b22; + color: #c9d1d9; + border: 1px solid #30363d; + border-radius: 6px; + padding: 6px 12px; + font-size: 13px; + cursor: pointer; +} + +.refresh-btn { + background: #238636; + color: #fff; + border: none; + border-radius: 6px; + padding: 6px 16px; + font-size: 13px; + font-weight: 600; + cursor: pointer; + transition: background 0.2s; +} + +.refresh-btn:hover { + background: #2ea043; +} + +/* Stats */ +.stats-section { + margin-bottom: 20px; +} + +/* Main Content */ +.main-content { + display: grid; + grid-template-columns: 1fr 340px; + gap: 20px; + margin-bottom: 20px; +} + +.chart-container { + background: #0d1117; + border-radius: 12px; + border: 1px solid #21262d; + overflow: hidden; +} + +.panel-container { + min-width: 0; +} + +/* Legend */ +.legend { + display: flex; + gap: 20px; + flex-wrap: wrap; + padding: 12px 16px; + background: #161b22; + border-radius: 8px; +} + +.legend-item { + display: flex; + align-items: center; + gap: 6px; + font-size: 12px; + color: #8b949e; +} + +.legend-color { + width: 12px; + height: 3px; + border-radius: 2px; +} + +/* Loading */ +.loading { + display: flex; + align-items: center; + justify-content: center; + gap: 12px; + padding: 80px 0; + color: #8b949e; + font-size: 14px; +} + +.spinner { + width: 20px; + height: 20px; + border: 2px solid #30363d; + border-top-color: #58a6ff; + border-radius: 50%; + animation: spin 0.8s linear infinite; +} + +@keyframes spin { + to { + transform: rotate(360deg); + } +} + +/* Error */ +.error { + display: flex; + align-items: center; + justify-content: center; + gap: 12px; + padding: 40px 0; + color: #ef5350; + font-size: 14px; +} + +.error button { + background: #21262d; + color: #c9d1d9; + border: 1px solid #30363d; + border-radius: 6px; + padding: 4px 12px; + cursor: pointer; +} + +/* Responsive */ +@media (max-width: 900px) { + .main-content { + grid-template-columns: 1fr; + } +} diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 000000000000..51654800559d --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,136 @@ +import { useEffect, useState } from "react"; +import Chart from "./components/Chart"; +import SignalPanel from "./components/SignalPanel"; +import StatsBar from "./components/StatsBar"; +import { fetchStrategyData } from "./api"; +import type { StrategyResponse } from "./types"; +import "./App.css"; + +function App() { + const [data, setData] = useState(null); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const [numCandles, setNumCandles] = useState(500); + + const loadData = async (candles: number) => { + setLoading(true); + setError(null); + try { + const result = await fetchStrategyData(candles); + setData(result); + } catch (err) { + setError( + err instanceof Error ? err.message : "Failed to fetch strategy data" + ); + } finally { + setLoading(false); + } + }; + + useEffect(() => { + loadData(numCandles); + }, [numCandles]); + + return ( +
+ {/* Header */} +
+
+

VWAPBB Strategy

+ XAUUSD + 5M +
+
+ + +
+
+ + {/* Content */} + {loading && ( +
+
+ Loading strategy data... +
+ )} + + {error && ( +
+ Error: {error} + +
+ )} + + {data && !loading && ( + <> + {/* Stats Bar */} +
+ +
+ + {/* Main Content */} +
+
+ +
+
+ +
+
+ + {/* Legend */} +
+
+
+ Bollinger Bands (20, 2) +
+
+
+ VWAP +
+
+
+ EMA 200 (15m) +
+
+
+ Buy Signal +
+
+
+ Sell Signal +
+
+ + )} +
+ ); +} + +export default App; diff --git a/frontend/src/api.ts b/frontend/src/api.ts new file mode 100644 index 000000000000..d49cd80e29ba --- /dev/null +++ b/frontend/src/api.ts @@ -0,0 +1,14 @@ +import axios from "axios"; +import type { StrategyResponse } from "./types"; + +const API_BASE = import.meta.env.VITE_API_URL || "http://localhost:8000"; + +export async function fetchStrategyData( + numCandles: number = 500 +): Promise { + const response = await axios.get( + `${API_BASE}/api/strategy`, + { params: { num_candles: numCandles } } + ); + return response.data; +} diff --git a/frontend/src/components/Chart.tsx b/frontend/src/components/Chart.tsx new file mode 100644 index 000000000000..719b281811b0 --- /dev/null +++ b/frontend/src/components/Chart.tsx @@ -0,0 +1,189 @@ +import { useEffect, useRef } from "react"; +import { + createChart, + type IChartApi, + ColorType, + CrosshairMode, + type CandlestickData, + type LineData, + type Time, + CandlestickSeries, + LineSeries, + createSeriesMarkers, +} from "lightweight-charts"; +import type { StrategyResponse } from "../types"; + +interface ChartProps { + data: StrategyResponse; +} + +export default function Chart({ data }: ChartProps) { + const chartContainerRef = useRef(null); + const chartRef = useRef(null); + + useEffect(() => { + if (!chartContainerRef.current || !data) return; + + if (chartRef.current) { + chartRef.current.remove(); + chartRef.current = null; + } + + const chart = createChart(chartContainerRef.current, { + layout: { + background: { type: ColorType.Solid, color: "#0d1117" }, + textColor: "#c9d1d9", + }, + grid: { + vertLines: { color: "#21262d" }, + horzLines: { color: "#21262d" }, + }, + crosshair: { + mode: CrosshairMode.Normal, + }, + rightPriceScale: { + borderColor: "#30363d", + }, + timeScale: { + borderColor: "#30363d", + timeVisible: true, + secondsVisible: false, + }, + width: chartContainerRef.current.clientWidth, + height: 500, + }); + + chartRef.current = chart; + + // Candlestick series + const candlestickSeries = chart.addSeries(CandlestickSeries, { + upColor: "#26a69a", + downColor: "#ef5350", + borderUpColor: "#26a69a", + borderDownColor: "#ef5350", + wickUpColor: "#26a69a", + wickDownColor: "#ef5350", + }); + + const candleData: CandlestickData[] = data.candles.map((c) => ({ + time: c.timestamp as Time, + open: c.open, + high: c.high, + low: c.low, + close: c.close, + })); + candlestickSeries.setData(candleData); + + // Bollinger Bands + const bbUpperSeries = chart.addSeries(LineSeries, { + color: "rgba(33, 150, 243, 0.5)", + lineWidth: 1, + title: "BB Upper", + }); + + const bbMiddleSeries = chart.addSeries(LineSeries, { + color: "rgba(33, 150, 243, 0.3)", + lineWidth: 1, + lineStyle: 2, + title: "BB Middle", + }); + + const bbLowerSeries = chart.addSeries(LineSeries, { + color: "rgba(33, 150, 243, 0.5)", + lineWidth: 1, + title: "BB Lower", + }); + + const bbUpperData: LineData[] = []; + const bbMiddleData: LineData[] = []; + const bbLowerData: LineData[] = []; + + data.indicators.forEach((ind) => { + if (ind.bb_upper !== null) { + bbUpperData.push({ time: ind.timestamp as Time, value: ind.bb_upper }); + } + if (ind.bb_middle !== null) { + bbMiddleData.push({ + time: ind.timestamp as Time, + value: ind.bb_middle, + }); + } + if (ind.bb_lower !== null) { + bbLowerData.push({ time: ind.timestamp as Time, value: ind.bb_lower }); + } + }); + + bbUpperSeries.setData(bbUpperData); + bbMiddleSeries.setData(bbMiddleData); + bbLowerSeries.setData(bbLowerData); + + // VWAP + const vwapSeries = chart.addSeries(LineSeries, { + color: "#ff9800", + lineWidth: 2, + title: "VWAP", + }); + + const vwapData: LineData[] = []; + data.indicators.forEach((ind) => { + if (ind.vwap !== null) { + vwapData.push({ time: ind.timestamp as Time, value: ind.vwap }); + } + }); + vwapSeries.setData(vwapData); + + // EMA 200 (horizontal line from 15m) + if (data.ema_200_15m !== null) { + const emaSeries = chart.addSeries(LineSeries, { + color: "#e040fb", + lineWidth: 2, + lineStyle: 2, + title: "EMA 200 (15m)", + }); + + const emaData: LineData[] = data.candles.map((c) => ({ + time: c.timestamp as Time, + value: data.ema_200_15m!, + })); + emaSeries.setData(emaData); + } + + // Signal markers + const markers = data.signals.map((signal) => ({ + time: signal.timestamp as Time, + position: signal.signal_type === "buy" ? ("belowBar" as const) : ("aboveBar" as const), + color: signal.signal_type === "buy" ? "#26a69a" : "#ef5350", + shape: signal.signal_type === "buy" ? ("arrowUp" as const) : ("arrowDown" as const), + text: signal.signal_type === "buy" ? "BUY" : "SELL", + })); + + createSeriesMarkers(candlestickSeries, markers); + + // Fit content + chart.timeScale().fitContent(); + + // Resize handler + const handleResize = () => { + if (chartContainerRef.current) { + chart.applyOptions({ + width: chartContainerRef.current.clientWidth, + }); + } + }; + + window.addEventListener("resize", handleResize); + + return () => { + window.removeEventListener("resize", handleResize); + chart.remove(); + chartRef.current = null; + }; + }, [data]); + + return ( +
+ ); +} diff --git a/frontend/src/components/SignalPanel.tsx b/frontend/src/components/SignalPanel.tsx new file mode 100644 index 000000000000..f7b849546edd --- /dev/null +++ b/frontend/src/components/SignalPanel.tsx @@ -0,0 +1,154 @@ +import type { Signal, StrategyResponse } from "../types"; + +interface SignalPanelProps { + data: StrategyResponse; +} + +function formatTime(timestamp: number): string { + return new Date(timestamp * 1000).toLocaleString(); +} + +function SignalCard({ signal }: { signal: Signal }) { + const isBuy = signal.signal_type === "buy"; + return ( +
+
+ + {signal.signal_type.toUpperCase()} @ ${signal.price.toFixed(2)} + + + {signal.scenario} + +
+
{signal.reason}
+
+ {formatTime(signal.timestamp)} +
+
+ ); +} + +export default function SignalPanel({ data }: SignalPanelProps) { + const trendColor = + data.trend === "bullish" + ? "#26a69a" + : data.trend === "bearish" + ? "#ef5350" + : "#8b949e"; + + return ( +
+ {/* Trend Status */} +
+

+ Market Trend (EMA 200 · 15m) +

+
+
+ + {data.trend.toUpperCase()} + +
+ {data.ema_200_15m !== null && ( +
+ EMA 200 (15m): ${data.ema_200_15m.toFixed(2)} +
+ )} +
+ + {/* Strategy Info */} +
+

+ Strategy Rules +

+
+ {data.trend === "bullish" ? ( + <> +
• Only BUY signals active
+
• Price must be above EMA 200
+
• Look for touches on VWAP or Lower BB
+
• Bullish engulfing pattern required
+ + ) : data.trend === "bearish" ? ( + <> +
• Only SELL signals active
+
• Price must be below EMA 200
+
• Look for touches on VWAP or Upper BB
+
• Bearish engulfing pattern required
+ + ) : ( +
• No clear trend — no signals generated
+ )} +
+
+ + {/* Signals */} +
+

+ Signals ({data.signals.length}) +

+
+ {data.signals.length === 0 ? ( +
+ No signals detected in the current data window. +
+ ) : ( + [...data.signals] + .reverse() + .map((signal, i) => ) + )} +
+
+
+ ); +} diff --git a/frontend/src/components/StatsBar.tsx b/frontend/src/components/StatsBar.tsx new file mode 100644 index 000000000000..632fc453c648 --- /dev/null +++ b/frontend/src/components/StatsBar.tsx @@ -0,0 +1,94 @@ +import type { StrategyResponse } from "../types"; + +interface StatsBarProps { + data: StrategyResponse; +} + +function StatCard({ + label, + value, + color, +}: { + label: string; + value: string; + color?: string; +}) { + return ( +
+
+ {label} +
+
+ {value} +
+
+ ); +} + +export default function StatsBar({ data }: StatsBarProps) { + const lastCandle = data.candles[data.candles.length - 1]; + const firstCandle = data.candles[0]; + const priceChange = lastCandle.close - firstCandle.open; + const priceChangePct = (priceChange / firstCandle.open) * 100; + + const buySignals = data.signals.filter((s) => s.signal_type === "buy").length; + const sellSignals = data.signals.filter( + (s) => s.signal_type === "sell" + ).length; + + const lastIndicator = data.indicators[data.indicators.length - 1]; + + return ( +
+ = 0 ? "#26a69a" : "#ef5350"} + /> + = 0 ? "+" : ""}${priceChange.toFixed(2)} (${priceChangePct.toFixed(2)}%)`} + color={priceChange >= 0 ? "#26a69a" : "#ef5350"} + /> + + + +
+ ); +} diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 000000000000..81a1eab1865a --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,25 @@ +* { + margin: 0; + padding: 0; + box-sizing: border-box; +} + +body { + background: #0d1117; + color: #c9d1d9; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +::-webkit-scrollbar { + width: 6px; +} + +::-webkit-scrollbar-track { + background: #161b22; +} + +::-webkit-scrollbar-thumb { + background: #30363d; + border-radius: 3px; +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 000000000000..10ed13e0c010 --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import { StrictMode } from "react"; +import { createRoot } from "react-dom/client"; +import "./index.css"; +import App from "./App.tsx"; + +createRoot(document.getElementById("root")!).render( + + + +); diff --git a/frontend/src/types.ts b/frontend/src/types.ts new file mode 100644 index 000000000000..a6c670945f79 --- /dev/null +++ b/frontend/src/types.ts @@ -0,0 +1,33 @@ +export interface Candle { + timestamp: number; + open: number; + high: number; + low: number; + close: number; + volume: number; +} + +export interface IndicatorValues { + timestamp: number; + ema_200: number | null; + vwap: number | null; + bb_upper: number | null; + bb_middle: number | null; + bb_lower: number | null; +} + +export interface Signal { + timestamp: number; + signal_type: "buy" | "sell"; + price: number; + scenario: string; + reason: string; +} + +export interface StrategyResponse { + candles: Candle[]; + indicators: IndicatorValues[]; + signals: Signal[]; + trend: "bullish" | "bearish" | "neutral"; + ema_200_15m: number | null; +} diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json new file mode 100644 index 000000000000..7f42e5f7cd2e --- /dev/null +++ b/frontend/tsconfig.app.json @@ -0,0 +1,25 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "es2023", + "lib": ["ES2023", "DOM"], + "module": "esnext", + "types": ["vite/client"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true + }, + "include": ["src"] +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 000000000000..1ffef600d959 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json new file mode 100644 index 000000000000..d3c52ea64c6c --- /dev/null +++ b/frontend/tsconfig.node.json @@ -0,0 +1,24 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "es2023", + "lib": ["ES2023"], + "module": "esnext", + "types": ["node"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true + }, + "include": ["vite.config.ts"] +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 000000000000..8b0f57b91aeb --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,7 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], +})