From 6e0b63cb3ea45c2deee167e5ca59fb855b1b1198 Mon Sep 17 00:00:00 2001
From: KhizarImran <khzrimrn@gmail.com>
Date: Sun, 14 Jun 2026 17:48:44 +0100
Subject: [PATCH] testing out python library integration

---
 .gitignore                                  |   5 +-
 python/__pycache__/backtest.cpython-314.pyc | Bin 0 -> 5017 bytes
 python/backtest.py                          |  69 ++++++++++++++++++++
 python/simple_strategy.py                   |  35 ++++++++++
 4 files changed, 107 insertions(+), 2 deletions(-)
 create mode 100644 python/__pycache__/backtest.cpython-314.pyc
 create mode 100644 python/backtest.py
 create mode 100644 python/simple_strategy.py

diff --git a/.gitignore b/.gitignore
index 8ce2613..9e88893 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,6 @@
 /target
 Cargo.lock
-### My claude md file 
+### My claude md file
 CLAUDE.md
-*/data/
+data/
+.env
diff --git a/python/__pycache__/backtest.cpython-314.pyc b/python/__pycache__/backtest.cpython-314.pyc
new file mode 100644
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diff --git a/python/backtest.py b/python/backtest.py
new file mode 100644
index 0000000..33015d0
--- /dev/null
+++ b/python/backtest.py
@@ -0,0 +1,69 @@
+import backtestingfx as _rust
+import pandas as pd
+
+
+class Strategy:
+    def init(self):
+        pass
+
+    def next(self):
+        pass
+
+    def buy(self, lot_size, stop_loss=None, take_profit=None):
+        self._broker.buy(self._bar.close, lot_size, self._bar.timestamp, stop_loss, take_profit)
+
+    def sell(self, lot_size, stop_loss=None, take_profit=None):
+        self._broker.sell(self._bar.close, lot_size, self._bar.timestamp, stop_loss, take_profit)
+
+    def close_all(self):
+        self._broker.close_all(self._bar.close, self._bar.timestamp)
+
+    def close_position(self, id):
+        self._broker.close_position(id, self._bar.close, self._bar.timestamp)
+
+
+class _Adapter:
+    def __init__(self, strategy):
+        self._strategy = strategy
+
+    def init(self, bars):
+        self._strategy.bars = bars
+        self._strategy.init()
+
+    def next(self, bar, broker):
+        self._strategy._bar = bar
+        self._strategy._broker = broker
+        self._strategy.next()
+
+
+class Backtest:
+    def __init__(self, df, strategy_class, cash=10000.0, commission=0.0, spread=0.0):
+        self._df = df
+        self._strategy_class = strategy_class
+        self._cash = cash
+        self._commission = commission
+        self._spread = spread
+
+    def _to_bars(self):
+        bars = []
+        for idx, row in self._df.iterrows():
+            if isinstance(idx, pd.Timestamp):
+                ts = int(idx.timestamp())
+            else:
+                ts = int(pd.Timestamp(row["timestamp"]).timestamp())
+
+            bars.append(_rust.Bar(
+                timestamp=ts,
+                open=float(row["open"]),
+                high=float(row["high"]),
+                low=float(row["low"]),
+                close=float(row["close"]),
+                volume=float(row.get("volume", 0.0))
+            ))
+        return bars
+
+    def run(self):
+        bars = self._to_bars()
+        engine = _rust.Engine(bars, self._cash, self._commission, self._spread)
+        strategy = self._strategy_class()
+        return engine.run(_Adapter(strategy))
diff --git a/python/simple_strategy.py b/python/simple_strategy.py
new file mode 100644
index 0000000..b25c64a
--- /dev/null
+++ b/python/simple_strategy.py
@@ -0,0 +1,35 @@
+import os
+
+import pandas as pd
+from backtest import Backtest, Strategy
+from dotenv import load_dotenv
+from lse import LSE
+
+load_dotenv()
+
+client = LSE(api_key=os.environ["LSE_API_KEY"])
+
+rows = client.candles("EUR/USD", "1h", limit=2000)
+df = pd.DataFrame(rows)
+df.to_csv("data/EURUSD_1H.csv", index=False)
+
+
+class BuyEveryBar(Strategy):
+    def next(self):
+        self.close_all()
+        self.buy(1.0)
+
+
+df = pd.read_csv("data/EURUSD_1H.csv")
+
+bt = Backtest(df, BuyEveryBar, cash=10000.0, commission=0.0, spread=0.0001)
+stats = bt.run()
+
+print(f"Return:       {stats.total_return_pct:.2f}%")
+print(f"Trades:       {stats.num_trades}")
+print(f"Win Rate:     {stats.win_rate_pct:.1f}%")
+print(f"Avg PnL:      {stats.avg_pnl:.5f}")
+print(f"Best Trade:   {stats.best_trade:.5f}")
+print(f"Worst Trade:  {stats.worst_trade:.5f}")
+print(f"Max Drawdown: {stats.max_drawdown_pct:.2f}%")
+print(f"Profit Factor:{stats.profit_factor:.2f}")