Metadata-Version: 2.4
Name: fast-trading-simulator
Version: 0.0.3
Summary: Numba accelerated minimalist trading simulator
Home-page: https://github.com/SerenaTradingResearch/fast-trading-simulator
Author: Ricky Ding
Author-email: e0134117@u.nus.edu
License: MIT
Keywords: trading,simulator,numba,quant,minimalist
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Office/Business :: Financial
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numba
Requires-Dist: numpy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary


## Intro

- `Numba` accelerated `minimalist` trading simulator
- In `28 lines`:
  - Multi-symbol
  - Multi-position, long & short, continuous from -1 to 1
  - Timeout, take-profit, stop-loss, trading fee
  - Initial cash, minimum cash, **allocation ratio (risk control)**

![](https://raw.githubusercontent.com/SerenaTradingResearch/fast-trading-simulator/refs/heads/main/test/simulate.png)

## Usage

```bash
pip install fast-trading-simulator
```

- [Simulation data (2025-07-01 to 2025-08-01)](https://raw.githubusercontent.com/SerenaTradingResearch/fast-trading-simulator/refs/heads/main/test/futures_sim_data_2025-07-01_2025-08-01.pkl)

```py
from typing import Dict

import numpy as np
from crypto_data_downloader.utils import load_pkl
from trading_models.utils import plot_general

from fast_trading_simulator.simulate import simulate

data: Dict = load_pkl("futures_sim_data_2025-07-01_2025-08-01.pkl", gz=True)
sim_data: Dict[str, Dict[str, np.ndarray]] = data["sim_data"]
# for sym, x in sim_data.items():
#     x["position"] = custom_strategy(x["close"])
symbols = list(sim_data.keys())
fields = list(sim_data["BTCUSDT"].keys())
arr = np.array([list(x.values()) for x in sim_data.values()])
arr = arr.transpose((2, 0, 1))

print(f"data keys: {list(data.keys())}")
print(f"arr.shape: {arr.shape} (time, symbols, fields)")
print(f"{len(symbols)} symbols: {symbols[:3]}...")
print(f"{len(fields)} fields: {fields}")
"""
timeout: int, number of time steps
take_profit: float, e.g. 0.01 (1%)
stop_loss: float, e.g. -0.3 (-30%)
fee: float, buy+sell total, e.g. 7e-4 (0.07%)
"""

data["sim_data"] = arr
trades = np.array(simulate(**data, init_cash=10e3, alloc_ratio=0.005))
plots = {
    f"worth ({len(trades)} trades)": trades[:, -1],
    "position_hist": trades[:, 2],
    "duration_hist": trades[:, -3],
    "profit_hist": trades[:, -2],
}
plot_general(plots, "simulate")

```

- Output

```bash
data keys: ['sim_data', 'timeout', 'take_profit', 'stop_loss', 'fee']
arr.shape: (8417, 482, 3) (time, symbols, fields)
482 symbols: ['BTCUSDT', 'ETHUSDT', 'BCHUSDT']...
3 fields: ['open_time', 'close', 'position']
```
