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• Comes with 11 pre-built strategies found in TradingStrats.py
• See Documentation below to implement your TA strategies.
• If you enjoy the repo please share it around to friends & tweet about it using the tweet button above 😃
  or Buy me a Coffee
• Utilizes python-Binance Client to execute orders and pull data from Binance
• Utilizes ta library for Technical indicators
• Utilizes plotly for generating interactive Trade graphs.
• Set up windows to sync time once a day if you don't do this binance will eventually reject orders with a timestamp error.

Strategies are implemented in Bot_Class as functions in Make_decision()

• Make_decision() must return Trade_Direction, stop_loss_val, take_profit_val for the strategy to work properly
• You might draw inspiration for a strategy from one of the samples in TradingStrats.py
  Create Custom Strategies
  Create Custom TP/SL functions

• Create a Binance Account (This link uses my referral which gives you 5% kickback on trades & would be greatly appreciated)
• Enable Two-factor Authentication in the security section to keep your crypto safe.
• Create a new API key under the API Management section.
• [✓] Read Info [✓] Enable Trading [✓] Enable Futures [X] Enable Withdrawals
• Whitelist your IP address to further secure your account, and so that your API keys don't expire after 3 months.
• Fill in your api keys into api_secret and api_key in Config_File.py

Run strategies at your own risk I am not responsible for your trading decisions, futures are risky and proper risk management should be adhered to at all times, always have a stoploss

python3 Live_Bot.py

• Settings are in Config_File.py
• Trade a single position at a time by setting Number_Of_Trades = 1, to trade multiple coins just increment this value.
• leverage and order_Size should be changed according to your preference
• symbol[] is a list of the symbols you wish to trade, If you wish to trade all symbols set Trade_All_Coins = True.
• Trailing stop: set use_trailing_stop = 1 and change trailing_stop_percent to suit your strategy to use the trailing stop (Min val .001 i.e .1%, Max 5 i.e. 5%). The trailing stop will be placed when the take profit value margin of increase/decrease is reached from your strategy.
• To close a trade based off a condition check_close_pos() must return a close_pos flag.
• strategy is the name of the strategy you want to use, see below for adding your own custom strategies.
• There are 11 default strategies to choose from: StochRSIMACD, tripleEMAStochasticRSIATR, tripleEMA, breakout, stochBB, goldenCross, candle_wick, fibMACD, EMA_cross, heikin_ashi_ema2 & heikin_ashi_ema
• TP_SL_choice correspond to the type of SL/TP seen in the dropdowns in the GUI see below for adding custom ones.
• SL_mult and TP_mult correspond to the numbers preceding the TP and SL choice dropdowns.
• Choose the Interval you want to trade and the buffer of candlesticks your strategy will need this will be dependent on indicators you need to ensure you have a sufficient buffer, or you will get errors.
• Buffer sizes should be about 5 times the length of your largest EMA. In TradingStrats.py you'll find all the strategies, taking tripleEMAStochasticRSIATR as an example:
  That strategy has a 50EMA as its largest EMA, so you need at least 250 candles. If you're trading the:
  • 1m timeframe this would be 250 minutes buffer
  • 3m timeframe would be 750 minutes buffer
  • 5m timeframe would be 1250 minutes buffer etc...
    (not sure that this is valid buffer input but you can convert these amounts to hours/ days)

Docker container creation:

• Navigate to the project directory in your terminal window
• Run the following Command this will create a docker image with the current state of your Config.py and choice of strategy in Bot_Class.py:

Sample docker commands that I use, note yours may be different (sudo may not be needed for you, I am on Linux):

sudo docker build --tag live_bot .
sudo docker run -it live_bot

• Run app.py to get the GUI

python3 app.py

Backtests are displayed in the terminal while running, the results are then saved to a csv file in the Backtests directory along with a graph image and trades taken graphs (see below).

• Otherwise run a backtest from the Backtester.py Script by configuring the inputs found in:

__name__ == "__main__":

Backtests are saved in a folder named backtests on completion of a backtest, along with a csv file of the trades taken. An interactive html file is generated for each trade (using plotly), so you can do analysis on winning and losing trades.

Silicon Trader | Trade Pro | Strategy Testing | Trading Journal | The Moving Average

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• Prioritise your Trello ticket with a one time payment
• Get Custom Strategies made to your specifications by reaching out to me on discord for a quote price.
• If you have any queries about anything, or need me to explain any blocks of code please reach out to me on Discord.
• If you have any suggestions or requests please reach out to me as well. Join My public Discord Server & Follow The Twitter