futu_algo: A Python repository from angkoonhian

billpwchan/futu-algo API Reference Documentation

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.
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)

[FutuOpenD.Config]
Host = <OpenD Host>
Port = <OpenD Port>
WebSocketPort = <OpenD WebSocketPort>
WebSocketKey = <OpenD WebSocketKey>
TrdEnv = <SIMULATE or REAL>

[FutuOpenD.Credential]
Username = <Futu Login Username>
Password_md5 = <Futu Login Password Md5 Value>

[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 = <Maximum % of Capital Allocated per Asset>
StockList = <Subscribed Stocks in List Format>

[Backtesting.Commission.HK]
FixedCharge = <Fixed Transaction Fee and Tax in HKD - 15.5>
PercCharge = <Percentage Transaction Fee in % - 0.1097>

[Email]
Port = <Server SMTP Setting>
SmtpServer = <Server SMTP Setting>
Sender = <Sender Email Address - account1@example.com>
Login = <Sender Email Address - account1@example.com>
Password = <Sender Email Password>
SubscriptionList = ["account1@example.com", "account2@example.com"]

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:

conda env create -f environment.yml

To export current environment, use the following command

conda env export > environment.yml

To update current environment with the latest dependencies, use the following command

conda env update --name futu_trade --file environment.yml --prune

For GitHub Actions - with pip dependencies, use the following command

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 😄

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"

python main_backend.py -f Volume_Threshold Price_Threshold -en MACD_Cross_Technique

GUI Usages

Start the GUI with main.py (NOT FINISHED YET)

python main.py

Future Plans

Contributor

Bill Chan -- Main Developer

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.

angkoonhian/futu_algo