# coin-test
[](https://github.com/coin-test/coin-test/actions?workflow=Tests)
[](https://codecov.io/gh/coin-test/coin-test)
Coin-test is a backtesting library designed for cryptocurrency trading. It supports trading strategies across multiple currencies and advanced configurations of tests, including cron-based scheduled execution of strategies, synthetic data generation, slippage modeling, and trading fees.
## Quick Start
Coin-test runs on Python 3.10 or higher. Install the package via pip:
```sh
pip3 install coin-test
```
To run a backtest, import the coin-test library. Then define your data source, strategy, and test settings to run the analysis.
```python
import datetime as dt
import os
import pandas as pd
from coin_test.backtest import Portfolio, Strategy, MarketTradeRequest
from coin_test.data import CustomDataset
from coin_test.util import AssetPair, Ticker, Money, Side
```
Then, import data from a CSV or another source to load
into the backtest.
```python
dataset_file = "data/ETHUSDT-1h-monthly/BTCUSDT-1h-2017-08.csv"
header = ["Open Time", "Open", "High", "Low", "Close", "Volume", "Close Time",
"Quote asset volume", "Number of trades", "Taker buy base asset volume",
"Taker buy quote asset volume", "Ignore"
]
df = pd.read_csv(dataset_file, names=header)
df = df.drop(columns=["Close Time", "Quote asset volume", "Number of trades",
"Taker buy base asset volume",
"Taker buy quote asset volume", "Ignore"])
df["Open Time"] //= 1000 # To seconds
df = df.sort_values(by=["Open Time"])
# define dataset metadata
usdt = Ticker("USDT")
btc = Ticker("BTC")
asset_pair = AssetPair(btc, usdt)
freq = "H"
dataset = CustomDataset(df, freq, asset_pair)
```
Strategies are stored in classes as shown below. Each strategy
should have a schedule, which is a cron string representing
when this strategy is run, a lookback, which is how much
data is accessed in the strategy, and a `__call__` method
which returns a list of TradeRequest objects, which represent
trades the strategy wants to make.
```python
class MACD(Strategy):
def __init__(self, asset_pair) -> None:
"""Initialize a MACD object."""
super().__init__(
name="MACD",
asset_pairs=[asset_pair],
schedule="0 9 * * *",
lookback=dt.timedelta(days=26),
)
self.perc = 0.2
def __call__(self, time, portfolio, lookback_data):
"""Execute test strategy."""
asset_pair = self.asset_pairs[0]
exp1 = lookback_data[asset_pair]["Close"].ewm(span=12 * 24, adjust=False).mean()
exp2 = lookback_data[asset_pair]["Close"].ewm(span=26 * 24, adjust=False).mean()
macd = exp1 - exp2
exp3 = macd.ewm(span=9 * 24, adjust=False).mean()
if macd.iloc[-1] > exp3.iloc[-1]:
return [MarketTradeRequest(
asset_pair,
Side.BUY,
notional=portfolio.available_assets(usdt).qty * self.perc,
)]
elif macd.iloc[-1] < exp3.iloc[-1]:
return [MarketTradeRequest(
asset_pair,
Side.SELL,
qty=portfolio.available_assets(btc).qty * self.perc,
)]
return []
```
This package supports multiple strategies, train-test splits
for historical data, synthetic data, and further customization.
To run the backtest, define the datasets
```python
from coin_test.backtest import ConstantSlippage, ConstantTransactionFeeCalculator
sc = ConstantSlippage(50)
tc = ConstantTransactionFeeCalculator(50)
datasets = [dataset]
strategies = [MACD]
results = coin_test.run(datasets, strategies, sc, tc,
portfolio, pd.Timedelta(days=90),
n_parallel=8)
```
Raw data
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"description": "# coin-test\n\n[](https://github.com/coin-test/coin-test/actions?workflow=Tests)\n[](https://codecov.io/gh/coin-test/coin-test)\n\nCoin-test is a backtesting library designed for cryptocurrency trading. It supports trading strategies across multiple currencies and advanced configurations of tests, including cron-based scheduled execution of strategies, synthetic data generation, slippage modeling, and trading fees.\n\n## Quick Start\n\nCoin-test runs on Python 3.10 or higher. Install the package via pip:\n\n```sh\npip3 install coin-test\n```\n\nTo run a backtest, import the coin-test library. Then define your data source, strategy, and test settings to run the analysis.\n\n```python\nimport datetime as dt\nimport os\nimport pandas as pd\n\nfrom coin_test.backtest import Portfolio, Strategy, MarketTradeRequest\nfrom coin_test.data import CustomDataset\nfrom coin_test.util import AssetPair, Ticker, Money, Side\n```\nThen, import data from a CSV or another source to load\ninto the backtest.\n```python\ndataset_file = \"data/ETHUSDT-1h-monthly/BTCUSDT-1h-2017-08.csv\"\nheader = [\"Open Time\", \"Open\", \"High\", \"Low\", \"Close\", \"Volume\", \"Close Time\",\n \"Quote asset volume\", \"Number of trades\", \"Taker buy base asset volume\",\n \"Taker buy quote asset volume\", \"Ignore\"\n]\ndf = pd.read_csv(dataset_file, names=header)\ndf = df.drop(columns=[\"Close Time\", \"Quote asset volume\", \"Number of trades\",\n \"Taker buy base asset volume\",\n \"Taker buy quote asset volume\", \"Ignore\"])\ndf[\"Open Time\"] //= 1000 # To seconds\ndf = df.sort_values(by=[\"Open Time\"])\n\n# define dataset metadata\nusdt = Ticker(\"USDT\")\nbtc = Ticker(\"BTC\")\nasset_pair = AssetPair(btc, usdt)\nfreq = \"H\"\n\ndataset = CustomDataset(df, freq, asset_pair)\n```\n\nStrategies are stored in classes as shown below. Each strategy\nshould have a schedule, which is a cron string representing\nwhen this strategy is run, a lookback, which is how much\ndata is accessed in the strategy, and a `__call__` method\nwhich returns a list of TradeRequest objects, which represent\ntrades the strategy wants to make.\n\n```python\nclass MACD(Strategy):\n def __init__(self, asset_pair) -> None:\n \"\"\"Initialize a MACD object.\"\"\"\n super().__init__(\n name=\"MACD\",\n asset_pairs=[asset_pair],\n schedule=\"0 9 * * *\",\n lookback=dt.timedelta(days=26),\n )\n self.perc = 0.2\n\n def __call__(self, time, portfolio, lookback_data):\n \"\"\"Execute test strategy.\"\"\"\n asset_pair = self.asset_pairs[0]\n exp1 = lookback_data[asset_pair][\"Close\"].ewm(span=12 * 24, adjust=False).mean()\n exp2 = lookback_data[asset_pair][\"Close\"].ewm(span=26 * 24, adjust=False).mean()\n macd = exp1 - exp2\n exp3 = macd.ewm(span=9 * 24, adjust=False).mean()\n\n if macd.iloc[-1] > exp3.iloc[-1]:\n return [MarketTradeRequest(\n asset_pair,\n Side.BUY,\n notional=portfolio.available_assets(usdt).qty * self.perc,\n )]\n elif macd.iloc[-1] < exp3.iloc[-1]:\n return [MarketTradeRequest(\n asset_pair,\n Side.SELL,\n qty=portfolio.available_assets(btc).qty * self.perc,\n )]\n return []\n```\n\nThis package supports multiple strategies, train-test splits\nfor historical data, synthetic data, and further customization.\nTo run the backtest, define the datasets\n\n```python\nfrom coin_test.backtest import ConstantSlippage, ConstantTransactionFeeCalculator\nsc = ConstantSlippage(50)\ntc = ConstantTransactionFeeCalculator(50)\n\ndatasets = [dataset]\nstrategies = [MACD]\n\nresults = coin_test.run(datasets, strategies, sc, tc,\n portfolio, pd.Timedelta(days=90),\n n_parallel=8)\n```\n",
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