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https://github.com/billpwchan/futu_algo
Futu Algorithmic Trading Solution (Python) 基於富途OpenAPI所開發量化交易程序
https://github.com/billpwchan/futu_algo
collaborate finance fintech futu-algo futu-api high-frequency-trading learn python trading trading-bot trading-platform trading-strategies
Last synced: 8 days ago
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Futu Algorithmic Trading Solution (Python) 基於富途OpenAPI所開發量化交易程序
- Host: GitHub
- URL: https://github.com/billpwchan/futu_algo
- Owner: billpwchan
- License: apache-2.0
- Created: 2020-12-22T18:22:50.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-02-23T11:27:20.000Z (4 months ago)
- Last Synced: 2024-04-17T06:06:18.937Z (2 months ago)
- Topics: collaborate, finance, fintech, futu-algo, futu-api, high-frequency-trading, learn, python, trading, trading-bot, trading-platform, trading-strategies
- Language: Python
- Homepage:
- Size: 142 MB
- Stars: 353
- Watchers: 15
- Forks: 121
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-github-star - futu_algo
README
**billpwchan/futu-algo API Reference Documentation**
[](https://app.codacy.com/gh/billpwchan/futu_algo?utm_source=github.com&utm_medium=referral&utm_content=billpwchan/futu_algo&utm_campaign=Badge_Grade_Settings)
[](https://github.com/billpwchan/futu_algo/issues)
[](https://github.com/billpwchan/futu_algo/blob/master/LICENSE)
[](https://github.com/billpwchan/futu_algo/blob/master/LICENSE)
[](https://github.com/billpwchan/futu_algo/commits/master)
[](https://github.com/billpwchan/futu_algo/commits/master)
[](https://github.com/billpwchan/futu_algo)
[](https://github.com/billpwchan/futu_algo)
## Highlights
- **Supported Platforms and Markets**
Futu_algo is a algorithmic trading solution developed based on FutuOpenD and FutuOpenAPI. Fully support FutuNiuNiu and
FutuMooMoo users in Hong Kong stock market. *(More market support is coming soon)*
- **Historical K-Line Data**
Allow users to automatically downloading historical data for your interested stocks into CSV and storing to SQLite
database for backtesting. *(up to 1M level for max. 2 years, or 1D level for max. 10 years)*
- **Backtesting Trading Strategies (BETA)**
Backtest your own trading strategies on historical data with a summarized reports and visualizations using Pyfolio.
For more demanding users, feel free to other commercial solutions such as Amibroker for backtesting.
- **Algorithmic Trading**
Real-time low-latency trading features that allows applying your own basket of trading strategies on your stock pool.
User can specify the trading strategy to be used for each stock based on their preference.
***EXAMPLE: 0.01s/STOCK TO DECIDE BUY/SELL ORDER WITH A 3-TECHNICAL INDICATORS STRATEGY (MACD, KDJ AND CLOSE PRICE)***
- **Advanced Stock Screener**
Screens high-quality stocks using your own stock screening strategies, and notify your friends using the email
subscription feature. **Feel free to subscribe by submitting this Google Form! https://forms.gle/C9y4kyYUArKmFzu86**
- **Trading Strategy Editor**
Write your own trading strategy following a simple template (buy, sell, calculate technical indicators). Common
strategies such as MACD and KDJ-based trading rules are provided as guidelines.
- **GUI Support (Upcoming)**
Easy-to-use GUI for users to adjust their configurations, trading, downloading data and filtering stocks within one
application. No longer need to type any command for trading!
## Version Guidance
| FutuAlgo Release | Futu OpenAPI Specification |
|:-----------------|:---------------------------|
| 1.0 | 6.1 |
## Deployment
### Pre-Requisite: Configuration File (Config.ini)
```ini
[FutuOpenD.Config]
Host =
Port =
WebSocketPort =
WebSocketKey =
TrdEnv =
[FutuOpenD.Credential]
Username =
Password_md5 =
[FutuOpenD.DataFormat]
HistoryDataFormat = ["code","time_key","open","close","high","low","pe_ratio","turnover_rate","volume","turnover","change_rate","last_close"]
SubscribedDataFormat = None
[TradePreference]
LotSizeMultiplier = <# of Stocks to Buy per Signal>
MaxPercPerAsset =
StockList =
[Backtesting.Commission.HK]
FixedCharge =
PercCharge =
[Email]
Port =
SmtpServer =
Sender =
Login =
Password =
SubscriptionList = ["[email protected]", "[email protected]"]
[TuShare.Credential]
token = 2134342ABC2D03780772038A7816
```
**IMPORTANT NOTE:** The format may be changed in later commits. Please refer to this README if exception is raised.
### 1. Install Dependencies
Install using [conda](https://docs.conda.io/en/latest/):
```bash
conda env create -f environment.yml
```
To export current environment, use the following command
```bash
conda env export > environment.yml
```
To update current environment with the latest dependencies, use the following command
```bash
conda env update --name futu_trade --file environment.yml --prune
```
For GitHub Actions - with pip dependencies, use the following command
```bash
pip list --format=freeze > requirements.txt
```
### 2. Install FutuOpenD
For **Windows/MacOS/CentOS/Ubuntu**:
https://www.futunn.com/download/OpenAPI
Please do make sure that you have at least a LV1 subscription level on your interested quotes. For details, please refer
to https://openapi.futunn.com/futu-api-doc/qa/quote.html
**MAKE SURE YOU LOGIN TO FUTU OPEND FIRST BEFORE STARTING FUTU_ALGO!**
### 4. Download Data (e.g. 1M Data for max. 2 Years)
For **Windows**:
python main_backend.py --force_update
For **MacOS/Linux**:
python3 main_backend.py --force_update
### 4. Enjoy :smile:
## Command-line Interface Usages
### Historical Data Download & Processing
Update all `K_1M` and `K_DAY` interval historical K-line data
python main_backend.py -u / python main_backend.py --update
**IMPORTANT NOTE:** This will not override existing historical data if the file exists. It will automatically detect
the latest stock data you have downloaded in the folder and resume from there.
If you want to refresh all data, use the following command instead (WITH CAUTION!)
python main_backend.py -fu / python main_backend.py --force_update
### Algorithmic Trading
Execute Algorithmic Trading with a Pre-defined Strategy (By default use **1M data**)
python main_backend.py -s MACD_Cross / python main_backend.py --strategy MACD_Cross
If you would like to use another time interval based date (e.g., Day data), use the following command
python main_backend.py -s MACD_Cross --time_interval K_DAY
If you do not have a pre-defined stock list in `config.ini`, then you can just trade the Top 30 HSI stocks
python main_backend.py -s MACD_Cross --include_hsi --time_interval K_DAY
**IMPORTANT NOTE:** The supported time intervals are: K_1M, K_30M, K_5M, K_15M, K_30M, K_60M, K_DAY, K_WEEK, K_MON,
K_YEAR.
### Stock Filtering and Email Subscription
Execute Stock Filtering with Pre-defined Filtering Strategies with Email Title "MACD_Cross_Technique" in HK and
China (Shanghai and Shenzhen) Stock Market
python main_backend.py -f Volume_Threshold Price_Threshold -en MACD_Cross_Technique -m HK CHINA
## GUI Usages
Start the GUI with `main.py` (**NOT FINISHED YET**)
python main.py
## Future Plans
- [ ] [NEED A GREAT NAME FOR THIS ALGO TRADE!!](https://github.com/billpwchan/futu_algo/issues/23)
- [x] [Custom Backtesting Time Interval]()
- [x] [Dynamic Instantiation](https://github.com/billpwchan/futu_algo/issues/18)
-----------
## Contributor
[Bill Chan -- Main Developer](https://github.com/billpwchan/)
## Disclaimer
Futures, stocks and options trading involves substantial risk of loss and is not suitable for every investor. The
valuation of futures, stocks and options may fluctuate, and, as a result, clients may lose more than their original
investment. The impact of seasonal and geopolitical events is already factored into market prices. The highly leveraged
nature of futures trading means that small market movements will have a great impact on your trading account and this
can work against you, leading to large losses or can work for you, leading to large gains.
If the market moves against you, you may sustain a total loss greater than the amount you deposited into your account.
You are responsible for all the risks and financial resources you use and for the chosen trading system. You should not
engage in trading unless you fully understand the nature of the transactions you are entering into and the extent of
your exposure to loss. If you do not fully understand these risks you must seek independent advice from your financial
advisor.
All trading strategies are used at your own risk.
Any content in this repository should not be relied upon as advice or construed as providing recommendations of any
kind. It is your responsibility to confirm and decide which trades to make. Trade only with risk capital; that is, trade
with money that, if lost, will not adversely impact your lifestyle and your ability to meet your financial obligations.
Past results are no indication of future performance. In no event should the content of this correspondence be construed
as an express or implied promise or guarantee.
This repository and its author are not responsible for any losses incurred as a result of using any of our trading
strategies. Loss-limiting strategies such as stop loss orders may not be effective because market conditions or
technological issues may make it impossible to execute such orders. Likewise, strategies using combinations of options
and/or futures positions such as “spread” or “straddle” trades may be just as risky as simple long and short positions.
Information provided in this correspondence is intended solely for informational purposes and is obtained from sources
believed to be reliable. Information is in no way guaranteed. No guarantee of any kind is implied or possible where
projections of future conditions are attempted.