266 lines
11 KiB
Markdown
266 lines
11 KiB
Markdown
# Backtesting
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This page explains how to validate your strategy performance by using
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Backtesting.
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## Test your strategy with Backtesting
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Now you have good Buy and Sell strategies, you want to test it against
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real data. This is what we call
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[backtesting](https://en.wikipedia.org/wiki/Backtesting).
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Backtesting will use the crypto-currencies (pair) from your config file
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and load static tickers located in
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[/freqtrade/tests/testdata](https://github.com/freqtrade/freqtrade/tree/develop/freqtrade/tests/testdata).
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If the 5 min and 1 min ticker for the crypto-currencies to test is not
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already in the `testdata` folder, backtesting will download them
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automatically. Testdata files will not be updated until you specify it.
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The result of backtesting will confirm you if your bot has better odds of making a profit than a loss.
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The backtesting is very easy with freqtrade.
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### Run a backtesting against the currencies listed in your config file
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#### With 5 min tickers (Per default)
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```bash
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python3 ./freqtrade/main.py backtesting
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```
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#### With 1 min tickers
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```bash
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python3 ./freqtrade/main.py backtesting --ticker-interval 1m
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```
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#### Update cached pairs with the latest data
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```bash
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python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
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```
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#### With live data (do not alter your testdata files)
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```bash
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python3 ./freqtrade/main.py backtesting --live
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```
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#### Using a different on-disk ticker-data source
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```bash
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python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
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```
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#### With a (custom) strategy file
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```bash
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python3 ./freqtrade/main.py -s TestStrategy backtesting
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```
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Where `-s TestStrategy` refers to the class name within the strategy file `test_strategy.py` found in the `freqtrade/user_data/strategies` directory
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#### Exporting trades to file
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```bash
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python3 ./freqtrade/main.py backtesting --export trades
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```
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The exported trades can be read using the following code for manual analysis, or can be used by the plotting script `plot_dataframe.py` in the scripts folder.
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``` python
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import json
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from pathlib import Path
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import pandas as pd
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filename=Path('user_data/backtest_data/backtest-result.json')
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with filename.open() as file:
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data = json.load(file)
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columns = ["pair", "profit", "opents", "closets", "index", "duration",
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"open_rate", "close_rate", "open_at_end", "sell_reason"]
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df = pd.DataFrame(data, columns=columns)
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df['opents'] = pd.to_datetime(df['opents'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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)
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df['closets'] = pd.to_datetime(df['closets'],
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unit='s',
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utc=True,
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infer_datetime_format=True
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)
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```
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If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.
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#### Exporting trades to file specifying a custom filename
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```bash
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python3 ./freqtrade/main.py backtesting --export trades --export-filename=backtest_teststrategy.json
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```
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#### Running backtest with smaller testset
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Use the `--timerange` argument to change how much of the testset
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you want to use. The last N ticks/timeframes will be used.
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Example:
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```bash
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python3 ./freqtrade/main.py backtesting --timerange=-200
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```
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#### Advanced use of timerange
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Doing `--timerange=-200` will get the last 200 timeframes
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from your inputdata. You can also specify specific dates,
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or a range span indexed by start and stop.
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The full timerange specification:
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- Use last 123 tickframes of data: `--timerange=-123`
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- Use first 123 tickframes of data: `--timerange=123-`
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- Use tickframes from line 123 through 456: `--timerange=123-456`
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- Use tickframes till 2018/01/31: `--timerange=-20180131`
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- Use tickframes since 2018/01/31: `--timerange=20180131-`
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- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
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- Use tickframes between POSIX timestamps 1527595200 1527618600:
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`--timerange=1527595200-1527618600`
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#### Downloading new set of ticker data
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To download new set of backtesting ticker data, you can use a download script.
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If you are using Binance for example:
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- create a folder `user_data/data/binance` and copy `pairs.json` in that folder.
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- update the `pairs.json` to contain the currency pairs you are interested in.
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```bash
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mkdir -p user_data/data/binance
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cp freqtrade/tests/testdata/pairs.json user_data/data/binance
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```
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Then run:
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```bash
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python scripts/download_backtest_data.py --exchange binance
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```
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This will download ticker data for all the currency pairs you defined in `pairs.json`.
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- To use a different folder than the exchange specific default, use `--export user_data/data/some_directory`.
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- To change the exchange used to download the tickers, use `--exchange`. Default is `bittrex`.
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- To use `pairs.json` from some other folder, use `--pairs-file some_other_dir/pairs.json`.
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- To download ticker data for only 10 days, use `--days 10`.
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- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
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For help about backtesting usage, please refer to [Backtesting commands](#backtesting-commands).
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## Understand the backtesting result
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The most important in the backtesting is to understand the result.
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A backtesting result will look like that:
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```
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======================================== BACKTESTING REPORT =========================================
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| pair | buy count | avg profit % | total profit BTC | avg duration | profit | loss |
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|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
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| ETH/BTC | 44 | 0.18 | 0.00159118 | 50.9 | 44 | 0 |
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| LTC/BTC | 27 | 0.10 | 0.00051931 | 103.1 | 26 | 1 |
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| ETC/BTC | 24 | 0.05 | 0.00022434 | 166.0 | 22 | 2 |
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| DASH/BTC | 29 | 0.18 | 0.00103223 | 192.2 | 29 | 0 |
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| ZEC/BTC | 65 | -0.02 | -0.00020621 | 202.7 | 62 | 3 |
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| XLM/BTC | 35 | 0.02 | 0.00012877 | 242.4 | 32 | 3 |
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| BCH/BTC | 12 | 0.62 | 0.00149284 | 50.0 | 12 | 0 |
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| POWR/BTC | 21 | 0.26 | 0.00108215 | 134.8 | 21 | 0 |
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| ADA/BTC | 54 | -0.19 | -0.00205202 | 191.3 | 47 | 7 |
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| XMR/BTC | 24 | -0.43 | -0.00206013 | 120.6 | 20 | 4 |
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| TOTAL | 335 | 0.03 | 0.00175246 | 157.9 | 315 | 20 |
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2018-06-13 06:57:27,347 - freqtrade.optimize.backtesting - INFO -
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====================================== LEFT OPEN TRADES REPORT ======================================
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| pair | buy count | avg profit % | total profit BTC | avg duration | profit | loss |
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|:---------|------------:|---------------:|-------------------:|---------------:|---------:|-------:|
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| ETH/BTC | 3 | 0.16 | 0.00009619 | 25.0 | 3 | 0 |
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| LTC/BTC | 1 | -1.00 | -0.00020118 | 1085.0 | 0 | 1 |
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| ETC/BTC | 2 | -1.80 | -0.00071933 | 1092.5 | 0 | 2 |
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| DASH/BTC | 0 | nan | 0.00000000 | nan | 0 | 0 |
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| ZEC/BTC | 3 | -4.27 | -0.00256826 | 1301.7 | 0 | 3 |
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| XLM/BTC | 3 | -1.11 | -0.00066744 | 965.0 | 0 | 3 |
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| BCH/BTC | 0 | nan | 0.00000000 | nan | 0 | 0 |
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| POWR/BTC | 0 | nan | 0.00000000 | nan | 0 | 0 |
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| ADA/BTC | 7 | -3.58 | -0.00503604 | 850.0 | 0 | 7 |
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| XMR/BTC | 4 | -3.79 | -0.00303456 | 291.2 | 0 | 4 |
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| TOTAL | 23 | -2.63 | -0.01213062 | 750.4 | 3 | 20 |
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```
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The 1st table will contain all trades the bot made.
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The 2nd table will contain all trades the bot had to `forcesell` at the end of the backtest period to prsent a full picture.
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These trades are also included in the first table, but are extracted separately for clarity.
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The last line will give you the overall performance of your strategy,
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here:
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```
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TOTAL 419 -0.41 -0.00348593 52.9
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```
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We understand the bot has made `419` trades for an average duration of
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`52.9` min, with a performance of `-0.41%` (loss), that means it has
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lost a total of `-0.00348593 BTC`.
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As you will see your strategy performance will be influenced by your buy
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strategy, your sell strategy, and also by the `minimal_roi` and
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`stop_loss` you have set.
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As for an example if your minimal_roi is only `"0": 0.01`. You cannot
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expect the bot to make more profit than 1% (because it will sell every
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time a trade will reach 1%).
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```json
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"minimal_roi": {
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"0": 0.01
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},
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```
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On the other hand, if you set a too high `minimal_roi` like `"0": 0.55`
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(55%), there is a lot of chance that the bot will never reach this
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profit. Hence, keep in mind that your performance is a mix of your
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strategies, your configuration, and the crypto-currency you have set up.
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## Backtesting multiple strategies
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To backtest multiple strategies, a list of Strategies can be provided.
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This is limited to 1 ticker-interval per run, however, data is only loaded once from disk so if you have multiple
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strategies you'd like to compare, this should give a nice runtime boost.
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All listed Strategies need to be in the same folder.
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``` bash
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freqtrade backtesting --timerange 20180401-20180410 --ticker-interval 5m --strategy-list Strategy001 Strategy002 --export trades
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```
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This will save the results to `user_data/backtest_data/backtest-result-<strategy>.json`, injecting the strategy-name into the target filename.
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There will be an additional table comparing win/losses of the different strategies (identical to the "Total" row in the first table).
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Detailed output for all strategies one after the other will be available, so make sure to scroll up.
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```
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=================================================== Strategy Summary ====================================================
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| Strategy | buy count | avg profit % | cum profit % | total profit ETH | avg duration | profit | loss |
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|:-----------|------------:|---------------:|---------------:|-------------------:|:----------------|---------:|-------:|
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| Strategy1 | 19 | -0.76 | -14.39 | -0.01440287 | 15:48:00 | 15 | 4 |
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| Strategy2 | 6 | -2.73 | -16.40 | -0.01641299 | 1 day, 14:12:00 | 3 | 3 |
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```
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## Next step
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Great, your strategy is profitable. What if the bot can give your the
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optimal parameters to use for your strategy?
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Your next step is to learn [how to find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
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