cyqnt-trd 0.1.9.dev1

A cryptocurrency trading toolkit centered on the standard_bot backtesting, paper signal, and execution workflow

cyqnt-trd

cyqnt-trd is a cryptocurrency trading toolkit centered on the standard_bot workflow for:

• historical data download to local parquet
• local resample from 1m into higher timeframes
• Numba-accelerated backtesting
• paper signal generation
• monitor / run-manager driven execution flows

The current recommended path is:

historical parquet -> local resample -> standard_bot signal -> NumbaBacktestRunner

Install

From PyPI

pip install cyqnt-trd

The package supports Python >=3.8,<3.12, which includes Binance AI's Python 3.11.2.

For special Linux deployments where Binance endpoints are signed by an internal CA that exists only in the system trust store, do not hardcode certifi.where() in application code. Instead let requests use the deployment CA bundle via environment variables, for example:

export REQUESTS_CA_BUNDLE=/etc/ssl/certs/ca-certificates.crt
export SSL_CERT_FILE=/etc/ssl/certs/ca-certificates.crt

For development

git clone https://github.com/nthu-chung/crypto_trading
cd crypto_trading
python3 -m venv .venv-standard-bot
source .venv-standard-bot/bin/activate
python -m pip install -U pip
python -m pip install -r requirements.txt -r requirements-standard-bot-mvp.txt

Recommended Standard Bot Entry Points

Backtest

python -m cyqnt_trd.standard_bot.entrypoints.mvp_backtest \
  --engine numba \
  --market-type futures \
  --strategy multi_timeframe_ma_spread \
  --symbol BTCUSDT \
  --interval 5m \
  --secondary-interval 1h \
  --primary-ma-period 20 \
  --reference-ma-period 20 \
  --spread-threshold-bps 0 \
  --historical-dir data/mtf_90d \
  --start-ts 1768003200000 \
  --end-ts 1775779200000 \
  --download-missing \
  --output-json docs/backtests/btc_mtf_ma_cross_5m_1h_20_20_90d.json

Paper Signal

python -m cyqnt_trd.standard_bot.entrypoints.mvp_paper \
  --market-type futures \
  --strategy multi_timeframe_ma_spread \
  --symbol BTCUSDT \
  --interval 5m \
  --secondary-interval 1h \
  --primary-ma-period 20 \
  --reference-ma-period 20 \
  --spread-threshold-bps 0 \
  --historical-dir data/mtf_90d \
  --dry-run

Monitor / Background Session

python -m cyqnt_trd.standard_bot.entrypoints.mvp_monitor_http \
  --broker paper \
  --host 127.0.0.1 \
  --port 8787

Data Workflow

The preferred data workflow is:

1. Download Binance K bars into local parquet
2. Store the finest useful granularity, usually 1m
3. Resample locally into 5m, 15m, 1h, etc.
4. Run standard_bot on local parquet instead of using raw API responses as final backtest input

This keeps:

• point-in-time alignment clearer
• local backtests repeatable
• paper signal and backtest logic consistent

Current Strategy Families On The Mainline

The standard_bot mainline currently includes Numba-backed support for:

• moving_average_cross
• price_moving_average
• rsi_reversion
• multi_timeframe_ma_spread
• donchian_breakout

Package Notes

• The preferred historical backtest engine is NumbaBacktestRunner
• The preferred CLI entrypoint is cyqnt_trd.standard_bot.entrypoints.mvp_backtest
• Legacy cyqnt_trd/backtesting/* still exists for compatibility, but is not the recommended path for new work

Requirements

Key dependencies include:

• pandas==2.3.3
• polars==1.0.0
• numba==0.60.0
• pyarrow==21.0.0
• requests==2.32.5

Binance SDK dependencies:

• binance-sdk-spot==8.2.1
• binance-sdk-derivatives-trading-usds-futures==10.0.1
• binance-sdk-algo==2.6.0
• binance-common==3.8.0
• binance-common

License

MIT License