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Merge branch 'develop' into feat/new_args_system

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Matthias
2019-11-03 10:09:49 +01:00
parent 0c3a8ddfb9 500d16620b
commit 6f01d7f8ea
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10
docs/configuration.md
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@@ -215,6 +215,11 @@ If this is configured, the following 4 values (`buy`, `sell`, `stoploss` and
`emergencysell` is an optional value, which defaults to `market` and is used when creating stoploss on exchange orders fails.
The below is the default which is used if this is not configured in either strategy or configuration file.
Since `stoploss_on_exchange` uses limit orders, the exchange needs 2 prices, the stoploss_price and the Limit price.
`stoploss` defines the stop-price - and limit should be slightly below this. This defaults to 0.99 / 1%.
Calculation example: we bought the asset at 100$.
Stop-price is 95$, then limit would be `95 * 0.99 = 94.05$` - so the stoploss will happen between 95$ and 94.05$.
Syntax for Strategy:
```python
@@ -224,7 +229,8 @@ order_types = {
"emergencysell": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
"stoploss_on_exchange_interval": 60
"stoploss_on_exchange_interval": 60,
"stoploss_on_exchange_limit_ratio": 0.99,
}
```
@@ -254,7 +260,7 @@ Configuration:
!!! Note
If `stoploss_on_exchange` is enabled and the stoploss is cancelled manually on the exchange, then the bot will create a new order.
!!! Warning stoploss_on_exchange failures
!!! Warning "Warning: stoploss_on_exchange failures"
If stoploss on exchange creation fails for some reason, then an "emergency sell" is initiated. By default, this will sell the asset using a market order. The order-type for the emergency-sell can be changed by setting the `emergencysell` value in the `order_types` dictionary - however this is not advised.
### Understand order_time_in_force

2
docs/data-download.md
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@@ -8,7 +8,7 @@ If no additional parameter is specified, freqtrade will download data for `"1m"`
Exchange and pairs will come from `config.json` (if specified using `-c/--config`).
Otherwise `--exchange` becomes mandatory.
!!! Tip Updating existing data
!!! Tip "Tip: Updating existing data"
If you already have backtesting data available in your data-directory and would like to refresh this data up to today, use `--days xx` with a number slightly higher than the missing number of days. Freqtrade will keep the available data and only download the missing data.
Be carefull though: If the number is too small (which would result in a few missing days), the whole dataset will be removed and only xx days will be downloaded.

22
docs/developer.md
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@@ -204,14 +204,15 @@ This part of the documentation is aimed at maintainers, and shows how to create
### Create release branch
``` bash
# make sure you're in develop branch
git checkout develop
First, pick a commit that's about one week old (to not include latest additions to releases).
``` bash
# create new branch
git checkout -b new_release
git checkout -b new_release <commitid>
```
Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.
* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7-1` should we need to do a second release that month.
* Commit this part
* push that branch to the remote and create a PR against the master branch
@@ -219,23 +220,18 @@ git checkout -b new_release
### Create changelog from git commits
!!! Note
Make sure that both master and develop are up-todate!.
Make sure that the master branch is uptodate!
``` bash
# Needs to be done before merging / pulling that branch.
git log --oneline --no-decorate --no-merges master..develop
git log --oneline --no-decorate --no-merges master..new_release
```
### Create github release / tag
Once the PR against master is merged (best right after merging):
* Use the button "Draft a new release" in the Github UI (subsection releases)
* Use the button "Draft a new release" in the Github UI (subsection releases).
* Use the version-number specified as tag.
* Use "master" as reference (this step comes after the above PR is merged).
* Use the above changelog as release comment (as codeblock)
### After-release
* Update version in develop by postfixing that with `-dev` (`2019.6 -> 2019.6-dev`).
* Create a PR against develop to update that branch.
* Use the above changelog as release comment (as codeblock).

2
docs/docker.md
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@@ -26,7 +26,7 @@ To update the image, simply run the above commands again and restart your runnin
Should you require additional libraries, please [build the image yourself](#build-your-own-docker-image).
!!! Note Docker image update frequency
!!! Note "Docker image update frequency"
The official docker images with tags `master`, `develop` and `latest` are automatically rebuild once a week to keep the base image uptodate.
In addition to that, every merge to `develop` will trigger a rebuild for `develop` and `latest`.

38
docs/faq.md
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@@ -55,6 +55,44 @@ If you have restricted pairs in your whitelist, you'll get a warning message in
If you're an "International" Customer on the Bittrex exchange, then this warning will probably not impact you.
If you're a US customer, the bot will fail to create orders for these pairs, and you should remove them from your Whitelist.
### How do I search the bot logs for something?
By default, the bot writes its log into stderr stream. This is implemented this way so that you can easily separate the bot's diagnostics messages from Backtesting, Edge and Hyperopt results, output from other various Freqtrade utility subcommands, as well as from the output of your custom `print()`'s you may have inserted into your strategy. So if you need to search the log messages with the grep utility, you need to redirect stderr to stdout and disregard stdout.
* In unix shells, this normally can be done as simple as:
```shell
$ freqtrade --some-options 2>&1 >/dev/null | grep 'something'
```
(note, `2>&1` and `>/dev/null` should be written in this order)
* Bash interpreter also supports so called process substitution syntax, you can grep the log for a string with it as:
```shell
$ freqtrade --some-options 2> >(grep 'something') >/dev/null
```
or
```shell
$ freqtrade --some-options 2> >(grep -v 'something' 1>&2)
```
* You can also write the copy of Freqtrade log messages to a file with the `--logfile` option:
```shell
$ freqtrade --logfile /path/to/mylogfile.log --some-options
```
and then grep it as:
```shell
$ cat /path/to/mylogfile.log | grep 'something'
```
or even on the fly, as the bot works and the logfile grows:
```shell
$ tail -f /path/to/mylogfile.log | grep 'something'
```
from a separate terminal window.
On Windows, the `--logfilename` option is also supported by Freqtrade and you can use the `findstr` command to search the log for the string of interest:
```
> type \path\to\mylogfile.log | findstr "something"
```
## Hyperopt module
### How many epoch do I need to get a good Hyperopt result?

23
docs/hyperopt.md
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@@ -23,17 +23,23 @@ Configuring hyperopt is similar to writing your own strategy, and many tasks wil
Depending on the space you want to optimize, only some of the below are required:
* fill `populate_indicators` - probably a copy from your strategy
* fill `buy_strategy_generator` - for buy signal optimization
* fill `indicator_space` - for buy signal optimzation
* fill `sell_strategy_generator` - for sell signal optimization
* fill `sell_indicator_space` - for sell signal optimzation
Optional, but recommended:
!!! Note
`populate_indicators` needs to create all indicators any of thee spaces may use, otherwise hyperopt will not work.
Optional - can also be loaded from a strategy:
* copy `populate_indicators` from your strategy - otherwise default-strategy will be used
* copy `populate_buy_trend` from your strategy - otherwise default-strategy will be used
* copy `populate_sell_trend` from your strategy - otherwise default-strategy will be used
!!! Note
Assuming the optional methods are not in your hyperopt file, please use `--strategy AweSomeStrategy` which contains these methods so hyperopt can use these methods instead.
Rarely you may also need to override:
* `roi_space` - for custom ROI optimization (if you need the ranges for the ROI parameters in the optimization hyperspace that differ from default)
@@ -156,7 +162,7 @@ that minimizes the value of the [loss function](#loss-functions).
The above setup expects to find ADX, RSI and Bollinger Bands in the populated indicators.
When you want to test an indicator that isn't used by the bot currently, remember to
add it to the `populate_indicators()` method in `hyperopt.py`.
add it to the `populate_indicators()` method in your custom hyperopt file.
## Loss-functions
@@ -270,6 +276,14 @@ For example, to use one month of data, pass the following parameter to the hyper
freqtrade hyperopt --timerange 20180401-20180501
```
### Running Hyperopt using methods from a strategy
Hyperopt can reuse `populate_indicators`, `populate_buy_trend`, `populate_sell_trend` from your strategy, assuming these methods are **not** in your custom hyperopt file, and a strategy is provided.
```bash
freqtrade hyperopt --strategy SampleStrategy --customhyperopt SampleHyperopt
```
### Running Hyperopt with Smaller Search Space
Use the `--spaces` argument to limit the search space used by hyperopt.
@@ -341,8 +355,7 @@ So for example you had `rsi-value: 29.0` so we would look at `rsi`-block, that t
(dataframe['rsi'] < 29.0)
```
Translating your whole hyperopt result as the new buy-signal
would then look like:
Translating your whole hyperopt result as the new buy-signal would then look like:
```python
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:

27
docs/installation.md
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@@ -95,29 +95,26 @@ sudo apt-get update
sudo apt-get install build-essential git
```
#### Raspberry Pi / Raspbian
### Raspberry Pi / Raspbian
Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is [miniconda](https://repo.continuum.io/miniconda/).
The following assumes the latest [Raspbian Buster lite image](https://www.raspberrypi.org/downloads/raspbian/) from at least September 2019.
This image comes with python3.7 preinstalled, making it easy to get freqtrade up and running.
The following assumes that miniconda3 is installed and available in your environment. Since the last miniconda3 installation file uses python 3.4, we will update to python 3.6 on this installation.
It's recommended to use (mini)conda for this as installation/compilation of `numpy` and `pandas` takes a long time.
Additional package to install on your Raspbian, `libffi-dev` required by cryptography (from python-telegram-bot).
Tested using a Raspberry Pi 3 with the Raspbian Buster lite image, all updates applied.
``` bash
conda config --add channels rpi
conda install python=3.6
conda create -n freqtrade python=3.6
conda activate freqtrade
conda install pandas numpy
sudo apt-get install python3-venv libatlas-base-dev
git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade
sudo apt install libffi-dev
python3 -m pip install -r requirements-common.txt
python3 -m pip install -e .
bash setup.sh -i
```
!!! Note "Installation duration"
Depending on your internet speed and the Raspberry Pi version, installation can take multiple hours to complete.
!!! Note
This does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
The above does not install hyperopt dependencies. To install these, please use `python3 -m pip install -e .[hyperopt]`.
We do not advise to run hyperopt on a Raspberry Pi, since this is a very resource-heavy operation, which should be done on powerful machine.
### Common

2
docs/plotting.md
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@@ -103,7 +103,7 @@ The `-p/--pairs` argument can be used to specify pairs you would like to plot.
Specify custom indicators.
Use `--indicators1` for the main plot and `--indicators2` for the subplot below (if values are in a different range than prices).
!!! tip
!!! Tip
You will almost certainly want to specify a custom strategy! This can be done by adding `-s Classname` / `--strategy ClassName` to the command.
``` bash

8
docs/rest-api.md
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@@ -16,11 +16,11 @@ Sample configuration:
},
```
!!! Danger Security warning
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger "Security warning"
By default, the configuration listens on localhost only (so it's not reachable from other systems). We strongly recommend to not expose this API to the internet and choose a strong, unique password, since others will potentially be able to control your bot.
!!! Danger Password selection
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
!!! Danger "Password selection"
Please make sure to select a very strong, unique password to protect your bot from unauthorized access.
You can then access the API by going to `http://127.0.0.1:8080/api/v1/version` to check if the API is running correctly.

54
docs/strategy-customization.md
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@@ -51,13 +51,13 @@ freqtrade trade --strategy AwesomeStrategy
**For the following section we will use the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py)
file as reference.**
!!! Note Strategies and Backtesting
!!! Note "Strategies and Backtesting"
To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware
that during backtesting the full time-interval is passed to the `populate_*()` methods at once.
It is therefore best to use vectorized operations (across the whole dataframe, not loops) and
avoid index referencing (`df.iloc[-1]`), but instead use `df.shift()` to get to the previous candle.
!!! Warning Using future data
!!! Warning "Warning: Using future data"
Since backtesting passes the full time interval to the `populate_*()` methods, the strategy author
needs to take care to avoid having the strategy utilize data from the future.
Some common patterns for this are listed in the [Common Mistakes](#common-mistakes-when-developing-strategies) section of this document.
@@ -330,12 +330,12 @@ if self.dp:
ticker_interval=inf_timeframe)
```
!!! Warning Warning about backtesting
!!! Warning "Warning about backtesting"
Be carefull when using dataprovider in backtesting. `historic_ohlcv()` (and `get_pair_dataframe()`
for the backtesting runmode) provides the full time-range in one go,
so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).
!!! Warning Warning in hyperopt
!!! Warning "Warning in hyperopt"
This option cannot currently be used during hyperopt.
#### Orderbook
@@ -405,6 +405,52 @@ if self.wallets:
- `get_used(asset)` - currently tied up balance (open orders)
- `get_total(asset)` - total available balance - sum of the 2 above
### Additional data (Trades)
A history of Trades can be retrieved in the strategy by querying the database.
At the top of the file, import Trade.
```python
from freqtrade.persistence import Trade
```
The following example queries for the current pair and trades from today, however other filters can easily be added.
``` python
if self.config['runmode'] in ('live', 'dry_run'):
trades = Trade.get_trades([Trade.pair == metadata['pair'],
Trade.open_date > datetime.utcnow() - timedelta(days=1),
Trade.is_open == False,
]).order_by(Trade.close_date).all()
# Summarize profit for this pair.
curdayprofit = sum(trade.close_profit for trade in trades)
```
Get amount of stake_currency currently invested in Trades:
``` python
if self.config['runmode'] in ('live', 'dry_run'):
total_stakes = Trade.total_open_trades_stakes()
```
Retrieve performance per pair.
Returns a List of dicts per pair.
``` python
if self.config['runmode'] in ('live', 'dry_run'):
performance = Trade.get_overall_performance()
```
Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).
``` json
{'pair': "ETH/BTC", 'profit': 0.015, 'count': 5}
```
!!! Warning
Trade history is not available during backtesting or hyperopt.
### Print created dataframe
To inspect the created dataframe, you can issue a print-statement in either `populate_buy_trend()` or `populate_sell_trend()`.

16
docs/strategy_analysis_example.md
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@@ -107,6 +107,22 @@ trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
trades.groupby("pair")["sell_reason"].value_counts()
```
## Analyze the loaded trades for trade parallelism
This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with `--disable-max-market-positions`.
`analyze_trade_parallelism()` returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.
```python
from freqtrade.data.btanalysis import analyze_trade_parallelism
# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades.plot()
```
## Plot results
Freqtrade offers interactive plotting capabilities based on plotly.

2
docs/telegram-usage.md
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@@ -93,7 +93,7 @@ Once all positions are sold, run `/stop` to completely stop the bot.
`/reload_conf` resets "max_open_trades" to the value set in the configuration and resets this command.
!!! warning
!!! Warning
The stop-buy signal is ONLY active while the bot is running, and is not persisted anyway, so restarting the bot will cause this to reset.
### /status