MoniGoMani
MoniGoMani aims to be more than just a conventional strategy, it's a framework to "easily" find a profitable strategy configuration in any market! Without the need to do any programming. However, you will need to know some Technical Analysis and be able to pull your own conclusions from your test-results, this is not just an easy copy/paste.
MGM (MoniGoMani) derives itself from other strategies by its use of something I called "weighted signals". Each signal has its own weight allocated to it & a total buy/sell signal needed is defined too. MGM will loop through all signals, if they trigger it will add up the weight and eventually it will check if it's bigger than what's needed in total over a candle lookback window (to take previous signals into consideration). If the grand total of the sum of weighted signals is bigger then what is required it will buy/sell. The signals used here are implemented, so they can easily be changed by a developer to further improve upon them.
The beauty lies in using MGM in combination with HyperOpting (= A form of machine learning where you BackTest a timerange a lot of times to find the most ideal values), since all weighted signals have been made HyperOptable it can be used to find the most "ideal" weight divisions. Also will it teach us what works where & what doesn't since MoniGoMani first detects Downwards/Sideways/Upwards trends and then does all the above individually for each kind of trend (Creating basically 3 individual strategies, 1 for each kind of trend).
Further it will do various HyperOptable checks upon the open trades to see if there are "bad" ones to unclog while running.
Go-To Commands
Hyper Opting MoniGoManiHyperStrategy.py:
freqtrade hyperopt -s MoniGoManiHyperStrategy -c ./user_data/mgm-config.json -c ./user_data/mgm-config-private.json --hyperopt-loss WinRatioAndProfitRatioLoss --spaces all -e 1000 --timerange 20210101-20210316
Apply HyperOpt Results from a <epoch of choice>:
freqtrade hyperopt-show -n <epoch of choice> -c ./user_data/mgm-config.json -c ./user_data/mgm-config-private.json --no-header --print-json | tail -n 1 | jq '.' > ./user_data/mgm-config-hyperopt.json
Reset HyperOpt Results:
rm ./user_data/mgm-config-hyperopt.json
Back Testing MoniGoManiHyperStrategy.py:
freqtrade backtesting -s MoniGoManiHyperStrategy -c ./user_data/mgm-config.json -c ./user_data/mgm-config-private.json --timerange 20210101-20210316
Total Average Signal Importance Calculation (with the Total-Overall-Signal-Importance-Calculator.py):
python ./user_data/mgm_tools/Total-Overall-Signal-Importance-Calculator.py -sc USDT -lf ./user_data/mgm-config-hyperopt.json -cf ./user_data/Total-Average-Signal-Importance-Report.log
Retrieve a current Binance-Top-Volume-StaticPairList.json file (using Binance-Retrieve-Top-Volume-StaticPairList.json):
freqtrade test-pairlist -c ./user_data/mgm_tools/Binance-Retrieve-Top-Volume-StaticPairList.json --quote USDT --print-json | tail -n 1 | jq '.|{exchange: { pair_whitelist: .}}' > ./user_data/mgm_pair_lists/Binance-USDT-Top-Volume-StaticPairList.json
# Don't forget to open the downloaded '...-StaticPairList.json' and copy the PairList Data into your own 'mgm-config.json' file to start using it!
Download Candle Data:
freqtrade download-data --timerange 20201201-20210316 -t 5m 1h -c ./user_data/mgm-config.json -c ./user_data/mgm-config-private.json
