From 33b14aaa2344b0fd95d1629627c3d135b24ae102 Mon Sep 17 00:00:00 2001
From: TheSiahxyz <164138827+TheSiahxyz@users.noreply.github.com>
Date: Wed, 1 Apr 2026 15:56:35 +0900
Subject: feat: initial trading platform implementation

Binance spot crypto trading platform with microservices architecture:
- shared: Pydantic models, Redis Streams broker, asyncpg DB layer
- data-collector: Binance WebSocket/REST market data collection
- strategy-engine: Plugin-based strategy execution (RSI, Grid)
- order-executor: Order execution with risk management
- portfolio-manager: Position tracking and PnL calculation
- backtester: Historical strategy testing with simulator
- cli: Click-based CLI for all operations
- Docker Compose orchestration with Redis and PostgreSQL
- 24 test files covering all modules
---
 .../strategy-engine/strategies/rsi_strategy.py     | 77 ++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 services/strategy-engine/strategies/rsi_strategy.py

(limited to 'services/strategy-engine/strategies/rsi_strategy.py')

diff --git a/services/strategy-engine/strategies/rsi_strategy.py b/services/strategy-engine/strategies/rsi_strategy.py
new file mode 100644
index 0000000..aebbafc
--- /dev/null
+++ b/services/strategy-engine/strategies/rsi_strategy.py
@@ -0,0 +1,77 @@
+from collections import deque
+from decimal import Decimal
+
+import pandas as pd
+
+from shared.models import Candle, Signal, OrderSide
+from strategies.base import BaseStrategy
+
+
+def _compute_rsi(series: pd.Series, period: int) -> float | None:
+    """Compute RSI using Wilder's smoothing (EMA-based)."""
+    if len(series) < period + 1:
+        return None
+    delta = series.diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    avg_gain = gain.ewm(com=period - 1, min_periods=period).mean()
+    avg_loss = loss.ewm(com=period - 1, min_periods=period).mean()
+    rs = avg_gain / avg_loss.replace(0, float("nan"))
+    rsi = 100 - (100 / (1 + rs))
+    value = rsi.iloc[-1]
+    if pd.isna(value):
+        return None
+    return float(value)
+
+
+class RsiStrategy(BaseStrategy):
+    name: str = "rsi"
+
+    def __init__(self) -> None:
+        self._closes: deque[float] = deque(maxlen=200)
+        self._period: int = 14
+        self._oversold: float = 30.0
+        self._overbought: float = 70.0
+        self._quantity: Decimal = Decimal("0.01")
+
+    def configure(self, params: dict) -> None:
+        self._period = int(params.get("period", 14))
+        self._oversold = float(params.get("oversold", 30))
+        self._overbought = float(params.get("overbought", 70))
+        self._quantity = Decimal(str(params.get("quantity", "0.01")))
+
+    def reset(self) -> None:
+        self._closes.clear()
+
+    def on_candle(self, candle: Candle) -> Signal | None:
+        self._closes.append(float(candle.close))
+
+        if len(self._closes) < self._period + 1:
+            return None
+
+        series = pd.Series(list(self._closes))
+        rsi_value = _compute_rsi(series, self._period)
+
+        if rsi_value is None:
+            return None
+
+        if rsi_value < self._oversold:
+            return Signal(
+                strategy=self.name,
+                symbol=candle.symbol,
+                side=OrderSide.BUY,
+                price=candle.close,
+                quantity=self._quantity,
+                reason=f"RSI {rsi_value:.2f} below oversold threshold {self._oversold}",
+            )
+        elif rsi_value > self._overbought:
+            return Signal(
+                strategy=self.name,
+                symbol=candle.symbol,
+                side=OrderSide.SELL,
+                price=candle.close,
+                quantity=self._quantity,
+                reason=f"RSI {rsi_value:.2f} above overbought threshold {self._overbought}",
+            )
+
+        return None
-- 
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