mirror of
https://github.com/KhizarImran/backtestingfx.git
synced 2026-07-28 12:37:43 +00:00
KhizarImran
161 lines
4.3 KiB
Markdown
161 lines
4.3 KiB
Markdown
# backtestingfx
|
|
|
|
[](https://pypi.org/project/backtestingfx/)
|
|
|
|
A Rust-powered FX backtesting library for Python. Write your strategy in Python, let Rust handle the heavy lifting.
|
|
|
|
Inspired by [backtesting.py](https://kernc.github.io/backtesting.py/) but built specifically for forex — lot sizes, pip-based PnL, stop loss, take profit, and realistic account currency conversion.
|
|
|
|
```
|
|
pip install backtestingfx
|
|
```
|
|
|
|
## Quick Start
|
|
|
|
```python
|
|
import pandas as pd
|
|
from backtestingfx import Backtest, Strategy
|
|
|
|
class MyCrossStrategy(Strategy):
|
|
def next(self):
|
|
self.close_all()
|
|
self.buy(lot_size=0.1)
|
|
|
|
df = pd.read_csv("EURUSD_1H.csv")
|
|
|
|
bt = Backtest(df, MyCrossStrategy, cash=10000.0, spread=0.0001)
|
|
stats = bt.run()
|
|
|
|
print(stats)
|
|
```
|
|
|
|
```
|
|
--- Backtest Results ---
|
|
Initial Cash: 10000.00
|
|
Final Cash: 9823.50
|
|
Total Return: -1.77%
|
|
Trades: 248
|
|
Win Rate: 52.4%
|
|
Avg PnL: -0.71380
|
|
Best Trade: 84.20000
|
|
Worst Trade: -61.30000
|
|
Profit Factor: 0.94
|
|
Max Drawdown: 3.21%
|
|
```
|
|
|
|
## Installation
|
|
|
|
```
|
|
pip install backtestingfx
|
|
```
|
|
|
|
Requires Python 3.9+.
|
|
|
|
## Writing a Strategy
|
|
|
|
Inherit from `Strategy` and implement `next()`. It is called once per bar.
|
|
|
|
```python
|
|
from backtestingfx import Backtest, Strategy
|
|
|
|
class MyStrategy(Strategy):
|
|
def init(self):
|
|
# called once before the loop starts
|
|
# self._bars contains all Bar objects if you need to pre-compute
|
|
pass
|
|
|
|
def next(self):
|
|
# self._bar — current bar (open, high, low, close, volume, timestamp)
|
|
# self._broker — the broker instance (advanced use)
|
|
|
|
if self._bar.close > 1.1000:
|
|
self.buy(lot_size=0.1, stop_loss=1.0950, take_profit=1.1100)
|
|
else:
|
|
self.close_all()
|
|
```
|
|
|
|
### Strategy methods
|
|
|
|
| Method | Description |
|
|
|--------|-------------|
|
|
| `self.buy(lot_size, stop_loss=None, take_profit=None)` | Open a long position |
|
|
| `self.sell(lot_size, stop_loss=None, take_profit=None)` | Open a short position |
|
|
| `self.close_all()` | Close all open positions |
|
|
| `self.close_position(id)` | Close a specific position by ID |
|
|
|
|
### Bar fields
|
|
|
|
```python
|
|
self._bar.open
|
|
self._bar.high
|
|
self._bar.low
|
|
self._bar.close
|
|
self._bar.volume
|
|
self._bar.timestamp # unix timestamp (int)
|
|
```
|
|
|
|
## Backtest Parameters
|
|
|
|
```python
|
|
Backtest(
|
|
df, # pandas DataFrame with OHLCV columns
|
|
StrategyClass,
|
|
cash=10000.0, # starting account balance in USD
|
|
commission=0.0, # commission per lot (e.g. 7.0 = $7/lot)
|
|
spread=0.0, # spread in price units (e.g. 0.0001 = 1 pip)
|
|
contract_size=100000.0, # standard FX lot size, don't change this
|
|
quote_to_account=1.0, # conversion rate from quote currency to USD
|
|
)
|
|
```
|
|
|
|
### Trading non-USD pairs
|
|
|
|
By default `quote_to_account=1.0` which is correct for USD-quoted pairs (EURUSD, GBPUSD).
|
|
|
|
For other pairs, pass the rate that converts the quote currency to USD:
|
|
|
|
| Pair | quote_to_account |
|
|
|------|-----------------|
|
|
| EURUSD, GBPUSD | `1.0` (default) |
|
|
| EURGBP | GBPUSD rate (e.g. `1.27`) |
|
|
| USDCAD, GBPCAD | CADUSD rate (e.g. `0.74`) |
|
|
| USDJPY | JPYUSD rate (e.g. `0.0067`) |
|
|
|
|
```python
|
|
bt = Backtest(df, MyStrategy, cash=10000.0, spread=0.00015, quote_to_account=1.27)
|
|
```
|
|
|
|
## Stats
|
|
|
|
| Field | Description |
|
|
|-------|-------------|
|
|
| `initial_cash` | Starting balance |
|
|
| `final_cash` | Ending balance |
|
|
| `total_return_pct` | Total return as a percentage |
|
|
| `num_trades` | Number of completed trades |
|
|
| `num_wins` | Number of winning trades |
|
|
| `win_rate_pct` | Win rate as a percentage |
|
|
| `avg_pnl` | Average PnL per trade in USD |
|
|
| `best_trade` | Best single trade PnL in USD |
|
|
| `worst_trade` | Worst single trade PnL in USD |
|
|
| `profit_factor` | Gross profit / gross loss |
|
|
| `max_drawdown_pct` | Maximum drawdown as a percentage |
|
|
|
|
## Data Format
|
|
|
|
Pass a pandas DataFrame with these columns:
|
|
|
|
```
|
|
open, high, low, close, volume
|
|
```
|
|
|
|
The index should be a `DatetimeIndex`, or include a `timestamp` column. Volume is optional (defaults to 0).
|
|
|
|
## Why Rust?
|
|
|
|
The backtesting engine is written in Rust and compiled as a native Python extension via [PyO3](https://pyo3.rs). This means the event loop, broker simulation, and stats computation run at native speed while your strategy stays in plain Python.
|
|
|
|
## License
|
|
|
|
MIT — see [LICENSE](LICENSE)
|