Optimize trading strategy using Freqtrade

Short demo on building, testing and optimizing a trading strategy using Freqtrade.

The DevBootstrap YouTube screencast supporting this repo is here. Enjoy! :)

Alias Docker-Compose Command

First, I recommend to alias docker-compose to dc and docker-compose run --rm "$@" to dcr to save of typing.

Put this in your ~/.bash_profile file so that its always aliased like this!

alias dc=docker-compose
dcr() { docker-compose run --rm "$@"; }

Now run source ~/.bash_profile.

Installing Freqtrade

Install and run via Docker.

Now install the necessary dependencies to run Freqtrade:

mkdir ft_userdata
cd ft_userdata/
# Download the dc file from the repository
curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml

# Pull the freqtrade image
dc pull

# Create user directory structure
dcr freqtrade create-userdir --userdir user_data

# Create configuration - Requires answering interactive questions
dcr freqtrade new-config --config user_data/config.json

NOTE: Any freqtrade commands are available by running dcr freqtrade <command> <optional arguments>. So the only difference to run the command via docker-compose is to prefix the command with our new alias dcr (which runs docker-compose run --rm "$@" ... see above for details.)

Config Bot

If you used the new-config sub-command (see above) when installing the bot, the installation script should have already created the default configuration file (config.json) for you.

The params that we will set to note are (from config.json). This allows all the available balance to be distrubuted accross all possible trades. So in dry run mode we have a default paper money balance of 1000 (can be changed using dry_run_wallet param) and if we set to have a max of 10 trades then Freqtrade would distribute the funds accrosss all 10 trades aprroximatly equally (1000 / 10 = 100 / trade).

"stake_amount" : "unlimited",
"tradable_balance_ratio": 0.99,

The above are used for Dry Runs and is the 'Dynamic Stake Amount'. For live trading you might want to change this. For example, only allow bot to trade 20% of excahnge account funds and cancel open orders on exit (if market goes crazy!)

"tradable_balance_ratio": 0.2,
"cancel_open_orders_on_exit": true

For details of all available parameters, please refer to the configuration parameters docs.

Create a Strategy

So I've created a 'BBRSINaiveStrategy' based on RSI and Bollenger Bands. Take a look at the file bbrsi_naive_strategy.py file for details.

To tell your instance of Freqtrade about this strategy, open your docker-compose.yml file and update the strategy flag (last flag of the command) to --strategy BBRSINaiveStrategy

For more details on Strategy Customization, please refer to the Freqtrade Docs

Remove past trade data

If you have run the bot already, you will need to clear out any existing dry run trades from the database. The easiest way to do this is to delete the sqlite database by running the command rm user_data/tradesv3.sqlite.

Sandbox / Dry Run

As a quick sanity check, you can now immediately start the bot in a sandbox mode and it will start trading (with paper money - not real money!).

To start trading in sandbox mode, simply start the service as a daemon using Docker Compose, like so and follow the log trail as follows:

dc up -d
dc ps
dc logs -f

Setup a pairs file

We will use Binance so we create a data directory for binance and copy our pairs.json file into that directory:

mkdir -p user_data/data/binance
cp pairs.json user_data/data/binance/.

Now put whatever pairs you are interested to download into the pairs.json file. Take a look at the pairs.json file included in this repo.

Download Data

Now that we have our pairs file in place, lets download the OHLCV data for backtesting our strategy.

dcr freqtrade download-data --exchange binance -t 15m

List the available data using the list-data sub-command:

dcr freqtrade list-data --exchange binance

Manually inspect the json files to examine the data is as expected (i.e. that it contains the expected OHLCV data requested).

List the available data for backtesting

Note to list the available data you need to pass the --data-format-ohlcv jsongz flag as below:

dcr freqtrade list-data --exchange binance

Backtest

Now we have the data for 1h and 4h OHLCV data for our pairs lets Backtest this strategy:

dcr freqtrade backtesting --datadir user_data/data/binance --export trades  --stake-amount 100 -s BBRSINaiveStrategy -i 15m

For details on interpreting the result, refer to 'Understading the backtesting result'

Plotting

Plot the results to see how the bot entered and exited trades over time. Remember to change the Docker image being referenced in the docker-compose file to freqtradeorg/freqtrade:develop_plot before running the below command.

Note that the plot_config that is contained in the strategy will be applied to the chart.

dcr freqtrade plot-dataframe --strategy BBRSINaiveStrategy -p ALGO/USDT -i 15m

Once the plot is ready you will see the message Stored plot as /freqtrade/user_data/plot/freqtrade-plot-ALGO_USDT-15m.html which you can open in a browser window.

Optimize

To optimize the strategy we will use the Hyperopt module of freqtrade. First up we need to create a new hyperopt file from a template:

dcr freqtrade new-hyperopt --hyperopt BBRSIHyperopt

Now add desired definitions for buy/sell guards and triggers to the Hyperopt file. Then run the optimization like so (NOTE: set the time interval and the number of epochs to test using the -i and -e flags:

dcr freqtrade hyperopt --hyperopt BBRSIHyperopt --hyperopt-loss SharpeHyperOptLoss --strategy BBRSINaiveStrategy -i 15m

Update Strategy

Apply the suggested optimized results from the Hyperopt to the strategy. Either replace the current strategy or create a new 'optimized' strategy.

Backtest

Now we have updated our strategy based on the result from the hyperopt lets run a backtest again:

dcr freqtrade backtesting --datadir user_data/data/binance --export trades --stake-amount 100 -s BBRSIOptimizedStrategy -i 15m

Sandbox / Dry Run

Before you run the Dry Run, don't forget to check your local config.json file is configured. Particularly the dry_run is true, the dry_run_wallet is set to something reasonable (like 1000 USDT) and that the timeframe is set to the same that you have used when building and optimizing your strategy!

"max_open_trades": 10,
"stake_currency": "USDT",
"stake_amount" : "unlimited",
"tradable_balance_ratio": 0.99,
"fiat_display_currency": "USD",
"timeframe": "15min",
"dry_run": true,
"dry_run_wallet": 1000,

View Dry Run via Freq UI

For use with docker you will need to enable the api server in the Freqtrade config and set listen_ip_address to "0.0.0.0", and also set the username & password so that you can login like so:

...

"api_server": {
  "enabled": true,
  "listen_ip_address": "0.0.0.0",
  "username": "Freqtrader",
  "password": "secretpass!",
P
...

In the docker-compose.yml file also map the ports like so:

ports:
  - "127.0.0.1:8080:8080"

Then you can access the Freq UI via a browser at http://127.0.0.1:8080/. You can also access and control the bot via a REST API too!