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Correct instructions for calling a custom strategy file To paraphrase the change: Prior - to call a custom strategy -s the strategy file name within users_data/strategies/ directory After - to call a custom strategy -s the class name within the strategy within users_data/strategies/ directory
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Backtesting
This page explains how to validate your strategy performance by using Backtesting.
Table of Contents
Test your strategy with Backtesting
Now you have good Buy and Sell strategies, you want to test it against real data. This is what we call backtesting.
Backtesting will use the crypto-currencies (pair) from your config file
and load static tickers located in
/freqtrade/tests/testdata.
If the 5 min and 1 min ticker for the crypto-currencies to test is not
already in the testdata folder, backtesting will download them
automatically. Testdata files will not be updated until you specify it.
The result of backtesting will confirm you if your bot as more chance to make a profit than a loss.
The backtesting is very easy with freqtrade.
Run a backtesting against the currencies listed in your config file
With 5 min tickers (Per default)
python3 ./freqtrade/main.py backtesting --realistic-simulation
With 1 min tickers
python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
Update cached pairs with the latest data
python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
With live data (do not alter your testdata files)
python3 ./freqtrade/main.py backtesting --realistic-simulation --live
Using a different on-disk ticker-data source
python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
With a (custom) strategy file
python3 ./freqtrade/main.py -s TestStrategy backtesting
Where -s TestStrategy refers to the class name within the strategy file test_strategy.py found in the freqtrade/user_data/strategies directory
Exporting trades to file
python3 ./freqtrade/main.py backtesting --export trades
Running backtest with smaller testset
Use the --timerange argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
python3 ./freqtrade/main.py backtesting --timerange=-200
Advanced use of timerange
Doing --timerange=-200 will get the last 200 timeframes
from your inputdata. You can also specify specific dates,
or a range span indexed by start and stop.
The full timerange specification:
- Use last 123 tickframes of data:
--timerange=-123 - Use first 123 tickframes of data:
--timerange=123- - Use tickframes from line 123 through 456:
--timerange=123-456 - Use tickframes till 2018/01/31:
--timerange=-20180131 - Use tickframes since 2018/01/31:
--timerange=20180131- - Use tickframes since 2018/01/31 till 2018/03/01 :
--timerange=20180131-20180301
Update testdata directory To update your testdata directory, or download into another testdata directory:
mkdir -p user_data/data/testdata-20180113
cp freqtrade/tests/testdata/pairs.json user_data/data-20180113
cd user_data/data-20180113
Possibly edit pairs.json file to include/exclude pairs
python3 freqtrade/tests/testdata/download_backtest_data.py -p pairs.json
The script will read your pairs.json file, and download ticker data into the current working directory.
For help about backtesting usage, please refer to Backtesting commands.
Understand the backtesting result
The most important in the backtesting is to understand the result.
A backtesting result will look like that:
====================== BACKTESTING REPORT ================================
pair buy count avg profit % total profit BTC avg duration
-------- ----------- -------------- ------------------ --------------
ETH/BTC 56 -0.67 -0.00075455 62.3
LTC/BTC 38 -0.48 -0.00036315 57.9
ETC/BTC 42 -1.15 -0.00096469 67.0
DASH/BTC 72 -0.62 -0.00089368 39.9
ZEC/BTC 45 -0.46 -0.00041387 63.2
XLM/BTC 24 -0.88 -0.00041846 47.7
NXT/BTC 24 0.68 0.00031833 40.2
POWR/BTC 35 0.98 0.00064887 45.3
ADA/BTC 43 -0.39 -0.00032292 55.0
XMR/BTC 40 -0.40 -0.00032181 47.4
TOTAL 419 -0.41 -0.00348593 52.9
The last line will give you the overall performance of your strategy, here:
TOTAL 419 -0.41 -0.00348593 52.9
We understand the bot has made 419 trades for an average duration of
52.9 min, with a performance of -0.41% (loss), that means it has
lost a total of -0.00348593 BTC.
As you will see your strategy performance will be influenced by your buy
strategy, your sell strategy, and also by the minimal_roi and
stop_loss you have set.
As for an example if your minimal_roi is only "0": 0.01. You cannot
expect the bot to make more profit than 1% (because it will sell every
time a trade will reach 1%).
"minimal_roi": {
"0": 0.01
},
On the other hand, if you set a too high minimal_roi like "0": 0.55
(55%), there is a lot of chance that the bot will never reach this
profit. Hence, keep in mind that your performance is a mix of your
strategies, your configuration, and the crypto-currency you have set up.
Next step
Great, your strategy is profitable. What if the bot can give your the
optimal parameters to use for your strategy?
Your next step is to learn how to find optimal parameters with Hyperopt