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Merge branch 'develop' into list-pairs2

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2019-10-20 23:22:45 +03:00
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10
docs/backtesting.md
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@@ -103,12 +103,6 @@ The full timerange specification:
- Use tickframes since 2018/01/31 till 2018/03/01 : `--timerange=20180131-20180301`
- Use tickframes between POSIX timestamps 1527595200 1527618600:
`--timerange=1527595200-1527618600`
- 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`
!!! warning
Be carefull when using non-date functions - these do not allow you to specify precise dates, so if you updated the test-data it will probably use a different dataset.
## Understand the backtesting result
@@ -195,6 +189,7 @@ Hence, keep in mind that your performance is an integral mix of all different el
Since backtesting lacks some detailed information about what happens within a candle, it needs to take a few assumptions:
- Buys happen at open-price
- Sell signal sells happen at open-price of the following candle
- Low happens before high for stoploss, protecting capital first.
- ROI sells are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the sell will be at 2%)
- Stoploss sells happen exactly at stoploss price, even if low was lower
@@ -203,6 +198,9 @@ Since backtesting lacks some detailed information about what happens within a ca
- Low uses the adjusted stoploss (so sells with large high-low difference are backtested correctly)
- Sell-reason does not explain if a trade was positive or negative, just what triggered the sell (this can look odd if negative ROI values are used)
Taking these assumptions, backtesting tries to mirror real trading as closely as possible. However, backtesting will **never** replace running a strategy in dry-run mode.
Also, keep in mind that past results don't guarantee future success.
### Further backtest-result analysis
To further analyze your backtest results, you can [export the trades](#exporting-trades-to-file).

4
docs/bot-usage.md
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@@ -106,7 +106,7 @@ user_data/
├── backtest_results
├── data
├── hyperopts
├── hyperopts_results
├── hyperopt_results
├── plot
└── strategies
```
@@ -256,7 +256,7 @@ optional arguments:
entry and exit).
--customhyperopt NAME
Specify hyperopt class name (default:
`DefaultHyperOpts`).
`DefaultHyperOpt`).
--hyperopt-path PATH Specify additional lookup path for Hyperopts and
Hyperopt Loss functions.
--eps, --enable-position-stacking

28
docs/data-download.md
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@@ -38,7 +38,7 @@ Mixing different stake-currencies is allowed for this file, since it's only used
]
```
### start download
### Start download
Then run:
@@ -57,6 +57,32 @@ This will download ticker data for all the currency pairs you defined in `pairs.
- Use `--timeframes` to specify which tickers to download. Default is `--timeframes 1m 5m` which will download 1-minute and 5-minute tickers.
- To use exchange, timeframe and list of pairs as defined in your configuration file, use the `-c/--config` option. With this, the script uses the whitelist defined in the config as the list of currency pairs to download data for and does not require the pairs.json file. You can combine `-c/--config` with most other options.
### Trades (tick) data
By default, `download-data` subcommand downloads Candles (OHLCV) data. Some exchanges also provide historic trade-data via their API.
This data can be useful if you need many different timeframes, since it is only downloaded once, and then resampled locally to the desired timeframes.
Since this data is large by default, the files use gzip by default. They are stored in your data-directory with the naming convention of `<pair>-trades.json.gz` (`ETH_BTC-trades.json.gz`). Incremental mode is also supported, as for historic OHLCV data, so downloading the data once per week with `--days 8` will create an incremental data-repository.
To use this mode, simply add `--dl-trades` to your call. This will swap the download method to download trades, and resamples the data locally.
Example call:
```bash
freqtrade download-data --exchange binance --pairs XRP/ETH ETH/BTC --days 20 --dl-trades
```
!!! Note
While this method uses async calls, it will be slow, since it requires the result of the previous call to generate the next request to the exchange.
!!! Warning
The historic trades are not available during Freqtrade dry-run and live trade modes because all exchanges tested provide this data with a delay of few 100 candles, so it's not suitable for real-time trading.
### Historic Kraken data
The Kraken API does only provide 720 historic candles, which is sufficient for FreqTrade dry-run and live trade modes, but is a problem for backtesting.
To download data for the Kraken exchange, using `--dl-trades` is mandatory, otherwise the bot will download the same 720 candles over and over, and you'll not have enough backtest data.
## Next step
Great, you now have backtest data downloaded, so you can now start [backtesting](backtesting.md) your strategy.

5
docs/edge.md
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@@ -249,13 +249,10 @@ freqtrade edge --stoplosses=-0.01,-0.1,-0.001 #min,max,step
freqtrade edge --timerange=20181110-20181113
```
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.
Doing `--timerange=-20190901` will get all available data until September 1st (excluding September 1st 2019).
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`

4
docs/faq.md
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@@ -38,7 +38,7 @@ like pauses. You can stop your bot, adjust settings and start it again.
### I want to improve the bot with a new strategy
That's great. We have a nice backtesting and hyperoptimizing setup. See
That's great. We have a nice backtesting and hyperoptimization setup. See
the tutorial [here|Testing-new-strategies-with-Hyperopt](bot-usage.md#hyperopt-commands).
### Is there a setting to only SELL the coins being held and not perform anymore BUYS?
@@ -59,7 +59,7 @@ If you're a US customer, the bot will fail to create orders for these pairs, and
### How many epoch do I need to get a good Hyperopt result?
Per default Hyperopts without `-e` or `--epochs` parameter will only
Per default Hyperopt called without the `-e`/`--epochs` command line option will only
run 100 epochs, means 100 evals of your triggers, guards, ... Too few
to find a great result (unless if you are very lucky), so you probably
have to run it for 10.000 or more. But it will take an eternity to

12
docs/hyperopt.md
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@@ -10,12 +10,12 @@ Hyperopt requires historic data to be available, just as backtesting does.
To learn how to get data for the pairs and exchange you're interrested in, head over to the [Data Downloading](data-download.md) section of the documentation.
!!! Bug
Hyperopt will crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
Hyperopt can crash when used with only 1 CPU Core as found out in [Issue #1133](https://github.com/freqtrade/freqtrade/issues/1133)
## Prepare Hyperopting
Before we start digging into Hyperopt, we recommend you to take a look at
an example hyperopt file located into [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py)
the sample hyperopt file located in [user_data/hyperopts/](https://github.com/freqtrade/freqtrade/blob/develop/user_data/hyperopts/sample_hyperopt.py).
Configuring hyperopt is similar to writing your own strategy, and many tasks will be similar and a lot of code can be copied across from the strategy.
@@ -64,9 +64,9 @@ multiple guards. The constructed strategy will be something like
"*buy exactly when close price touches lower bollinger band, BUT only if
ADX > 10*".
If you have updated the buy strategy, ie. changed the contents of
`populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must use.
If you have updated the buy strategy, i.e. changed the contents of
`populate_buy_trend()` method, you have to update the `guards` and
`triggers` your hyperopt must use correspondingly.
#### Sell optimization
@@ -82,7 +82,7 @@ To avoid naming collisions in the search-space, please prefix all sell-spaces wi
#### Using ticker-interval as part of the Strategy
The Strategy exposes the ticker-interval as `self.ticker_interval`. The same value is available as class-attribute `HyperoptName.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpts.ticker_interval`.
In the case of the linked sample-value this would be `SampleHyperOpt.ticker_interval`.
## Solving a Mystery

3
docs/requirements-docs.txt
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@@ -1 +1,2 @@
mkdocs-material==4.4.3
mkdocs-material==4.4.3
mdx_truly_sane_lists==1.2

17
docs/strategy-customization.md
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@@ -138,15 +138,19 @@ def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
return dataframe
```
!!! Note
Buying requires sellers to buy from - therefore volume needs to be > 0 (`dataframe['volume'] > 0`) to make sure that the bot does not buy/sell in no-activity periods.
### Sell signal rules
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
@@ -168,9 +172,10 @@ def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
return dataframe