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Update documentation

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Gerald Lonlas 2018-01-17 23:06:37 -08:00
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98
docs/bot-optimization.md
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@ -3,21 +3,55 @@ This page explains where to customize your strategies, and add new
indicators.
## Table of Contents
- [Change your strategy](#change-your-strategy)
- [Install a custom strategy file](#install-a-custom-strategy-file)
- [Customize your strategy](#change-your-strategy)
- [Add more Indicator](#add-more-indicator)
- [Where is the default strategy](#where-is-the-default-strategy)
Since the version `0.16.0` the bot allows using custom strategy file.
## Install a custom strategy file
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies`.
Let guess you have a strategy file `awesome-strategy.py`:
1. Move your file into `user_data/strategies` (you should have `user_data/strategies/awesome-strategy.py`
2. Start the bot with the param `--strategy awesome-strategy` (the parameter is the name of the file without '.py')
```bash
python3 ./freqtrade/main.py --strategy awesome_strategy
```
## Change your strategy
The bot is using buy and sell strategies to buy and sell your trades.
Both are customizable.
The bot includes a default strategy file. However, we recommend you to
use your own file to not have to lose your parameters everytime the default
strategy file will be updated on Github. Put your custom strategy file
into the folder `user_data/strategies`.
### Buy strategy
The default buy strategy is located in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73-L92).
Edit the function `populate_buy_trend()` to update your buy strategy.
A strategy file contains all the information needed to build a good strategy:
- Buy strategy rules
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Hyperopt parameter
Sample:
The bot also include a sample strategy you can update: `user_data/strategies/test_strategy.py`.
You can test it with the parameter: `--strategy test_strategy`
```bash
python3 ./freqtrade/main.py --strategy awesome_strategy
```
**For the following section we will use the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
file as reference.**
### Buy strategy
Edit the method `populate_buy_trend()` into your strategy file to
update your buy strategy.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@ -25,14 +59,9 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
@ -40,41 +69,31 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
```
### Sell strategy
The default buy strategy is located in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115)
Edit the function `populate_sell_trend()` to update your buy strategy.
Edit the method `populate_sell_trend()` into your strategy file to
update your sell strategy.
Sample:
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(crossed_above(dataframe['rsi'], 70)) |
(crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
(dataframe['tema'] > dataframe['blower']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicator
As you have seen, buy and sell strategies need indicators. You can see
the indicators in the file
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L95-L115).
Of course you can add more indicators by extending the list contained in
the function `populate_indicators()`.
As you have seen, buy and sell strategies need indicators. You can add
more indicators by extending the list contained in
the method `populate_indicators()` from your strategy file.
Sample:
```python
@ -111,6 +130,15 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
return dataframe
```
**Want more indicators example?**
Look into the [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py).
Then uncomment indicateur you need.
### Where is the default strategy?
The default buy strategy is located in the file
[freqtrade/default_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/strategy/default_strategy.py).
## Next step
Now you have a perfect strategy you probably want to backtesting it.

45
docs/bot-usage.md
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@ -22,19 +22,21 @@ positional arguments:
optional arguments:
-h, --help show this help message and exit
-c PATH, --config PATH
specify configuration file (default: config.json)
-v, --verbose be verbose
--version show program's version number and exit
-dd PATH, --datadir PATH
Path is from where backtesting and hyperopt will load the
ticker data files (default freqdata/tests/testdata).
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
-c PATH, --config PATH
specify configuration file (default: config.json)
-s PATH, --strategy PATH
specify strategy file (default:
freqtrade/strategy/default_strategy.py)
--dry-run-db Force dry run to use a local DB
"tradesv3.dry_run.sqlite" instead of memory DB. Work
only if dry_run is enabled.
-dd PATH, --datadir PATH
path to backtest data (default freqdata/tests/testdata
--dynamic-whitelist [INT]
dynamically generate and update whitelist based on 24h
BaseVolume (Default 20 currencies)
```
### How to use a different config file?
@ -45,6 +47,33 @@ default, the bot will load the file `./config.json`
python3 ./freqtrade/main.py -c path/far/far/away/config.json
```
### How to use --strategy?
This parameter will allow you to load your custom strategy file. Per
default without `--strategy` or `-s` the bol will load the
`default_strategy` included with the bot (`freqtrade/strategy/default_strategy.py`).
The bot will search your strategy file into `user_data/strategies` and
`freqtrade/strategy`.
To load a strategy, simply pass the file name (without .py) in this
parameters.
**Example:**
In `user_data/strategies` you have a file `my_awesome_strategy.py` to
load it:
```bash
python3 ./freqtrade/main.py --strategy my_awesome_strategy
```
If the bot does not find your strategy file, it will fallback to the
`default_strategy`.
Learn more about strategy file in [optimize your bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md).
#### How to install a strategy?
This is very simple. Copy paste your strategy file into the folder
`user_data/strategies`. And voila, the bot is ready to use it.
### How to use --dynamic-whitelist?
Per default `--dynamic-whitelist` will retrieve the 20 currencies based
on BaseVolume. This value can be changed when you run the script.

12
docs/configuration.md
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@ -20,8 +20,8 @@ The table below will list all configuration parameters.
| `ticker_interval` | [1, 5, 30, 60, 1440] | No | The ticker interval to use (1min, 5 min, 30 min, 1 hour or 1 day). Defaut is 5 minutes
| `fiat_display_currency` | USD | Yes | Fiat currency used to show your profits. More information below.
| `dry_run` | true | Yes | Define if the bot must be in Dry-run or production mode.
| `minimal_roi` | See below | Yes | Set the threshold in percent the bot will use to sell a trade. More information below.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below.
| `minimal_roi` | See below | No | Set the threshold in percent the bot will use to sell a trade. More information below. If set, this parameter will override `minimal_roi` from your strategy file.
| `stoploss` | -0.10 | No | Value of the stoploss in percent used by the bot. More information below. If set, this parameter will override `stoploss` from your strategy file.
| `unfilledtimeout` | 0 | No | How long (in minutes) the bot will wait for an unfilled order to complete, after which the order will be cancelled.
| `bid_strategy.ask_last_balance` | 0.0 | Yes | Set the bidding price. More information below.
| `exchange.name` | bittrex | Yes | Name of the exchange class to use.
@ -53,11 +53,19 @@ See the example below:
},
```
Most of the strategy files already include the optimal `minimal_roi`
value. This parameter is optional. If you use it, it will take over the
`minimal_roi` value from the strategy file.
### Understand stoploss
`stoploss` is loss in percentage that should trigger a sale.
For example value `-0.10` will cause immediate sell if the
profit dips below -10% for a given trade. This parameter is optional.
Most of the strategy files already include the optimal `stoploss`
value. This parameter is optional. If you use it, it will take over the
`stoploss` value from the strategy file.
### Understand initial_state
`initial_state` is an optional field that defines the initial application state.
Possible values are `running` or `stopped`. (default=`running`)

46
docs/hyperopt.md
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@ -14,14 +14,13 @@ parameters with Hyperopt.
## Prepare Hyperopt
Before we start digging in Hyperopt, we recommend you to take a look at
out Hyperopt file
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py)
your strategy file located into [user_data/strategies/](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py)
### 1. Configure your Guards and Triggers
There are two places you need to change to add a new buy strategy for
testing:
- Inside the [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L167-L207).
- Inside the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
There are two places you need to change in your strategy file to add a
new buy strategy for testing:
- Inside [populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L278-L294).
- Inside [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297) known as `SPACE`.
There you have two different type of indicators: 1. `guards` and 2.
`triggers`.
@ -38,10 +37,10 @@ ADX > 10*".
If you have updated the buy strategy, means change the content of
`populate_buy_trend()` function you have to update the `guards` and
`populate_buy_trend()` method you have to update the `guards` and
`triggers` hyperopts must used.
As for an example if your `populate_buy_trend()` function is:
As for an example if your `populate_buy_trend()` method is:
```python
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
dataframe.loc[
@ -56,10 +55,10 @@ Your hyperopt file must contains `guards` to find the right value for
`(dataframe['adx'] > 65)` & and `(dataframe['plus_di'] > 0.5)`. That
means you will need to enable/disable triggers.
In our case the `SPACE` and `populate_buy_trend` in hyperopt.py file
In our case the `SPACE` and `populate_buy_trend` in your strategy file
will be look like:
```python
SPACE = {
space = {
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
@ -82,7 +81,7 @@ SPACE = {
...
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if params['adx']['enabled']:
@ -111,13 +110,13 @@ cannot use your config file. It is also made on purpose to allow you
testing your strategy with different configurations.
The Hyperopt configuration is located in
[freqtrade/optimize/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt_conf.py).
[user_data/hyperopt_conf.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/hyperopt_conf.py).
## Advanced notions
### Understand the Guards and Triggers
When you need to add the new guards and triggers to be hyperopt
parameters, you do this by adding them into the [SPACE dict](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L47-L94).
parameters, you do this by adding them into the [hyperopt_space()](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py#L244-L297).
If it's a trigger, you add one line to the 'trigger' choice group and that's it.
@ -149,9 +148,8 @@ for best working algo.
### Add a new Indicators
If you want to test an indicator that isn't used by the bot currently,
you need to add it to
[freqtrade/analyze.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L40-L70)
inside the `populate_indicators` function.
you need to add it to your strategy file (example: [user_data/strategies/test_strategy.py](https://github.com/gcarq/freqtrade/blob/develop/user_data/strategies/test_strategy.py))
inside the `populate_indicators()` method.
## Execute Hyperopt
Once you have updated your hyperopt configuration you can run it.
@ -165,8 +163,8 @@ python3 ./freqtrade/main.py -c config.json hyperopt
### Execute hyperopt with different ticker-data source
If you would like to learn parameters using an alternate ticke-data that
you have on-disk, use the --datadir PATH option. Default hyperopt will
use data from directory freqtrade/tests/testdata.
you have on-disk, use the `--datadir PATH` option. Default hyperopt will
use data from directory `user_data/data`.
### Running hyperopt with smaller testset
@ -270,15 +268,11 @@ customizable value.
- and so on...
You have to look from
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L170-L200)
what those values match to.
You have to look inside your strategy file into `buy_strategy_generator()`
method, what those values match to.
So for example you had `adx:` with the `value: 15.0` so we would look
at `adx`-block from
[freqtrade/optimize/hyperopt.py](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/optimize/hyperopt.py#L178-L179).
That translates to the following code block to
[analyze.populate_buy_trend()](https://github.com/gcarq/freqtrade/blob/develop/freqtrade/analyze.py#L73)
at `adx`-block, that translates to the following code block:
```
(dataframe['adx'] > 15.0)
```
@ -286,7 +280,7 @@ That translates to the following code block to
So translating your whole hyperopt result to as the new buy-signal
would be the following:
```
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 15.0) & # adx-value

167
user_data/strategies/test_strategy.py
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@ -9,15 +9,20 @@ from pandas import DataFrame
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
# Update this variable if you change the class name
class_name = 'TestStrategy'
# This class is a sample. Feel free to customize it.
class TestStrategy(IStrategy):
"""
This is a test strategy to inspire you.
More information in https://github.com/gcarq/freqtrade/blob/develop/docs/bot-optimization.md
You can:
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
@ -51,10 +56,171 @@ class TestStrategy(IStrategy):
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Momentum Indicator
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
"""
# Awesome oscillator
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# Minus Directional Indicator / Movement
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# Plus Directional Indicator / Movement
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# ROC
dataframe['roc'] = ta.ROC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch
stoch = ta.STOCH(dataframe)
dataframe['slowd'] = stoch['slowd']
dataframe['slowk'] = stoch['slowk']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stoch RSI
stoch_rsi = ta.STOCHRSI(dataframe)
dataframe['fastd_rsi'] = stoch_rsi['fastd']
dataframe['fastk_rsi'] = stoch_rsi['fastk']
"""
# Overlap Studies
# ------------------------------------
"""
# Previous Bollinger bands
# Because ta.BBANDS implementation is broken with small numbers, it actually
# returns middle band for all the three bands. Switch to qtpylib.bollinger_bands
# and use middle band instead.
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
"""
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
"""
# EMA - Exponential Moving Average
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
"""
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
"""
# Hammer: values [0, 100]
dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# Inverted Hammer: values [0, 100]
dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# Dragonfly Doji: values [0, 100]
dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# Piercing Line: values [0, 100]
dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# Morningstar: values [0, 100]
dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# Three White Soldiers: values [0, 100]
dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
"""
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
"""
# Hanging Man: values [0, 100]
dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# Shooting Star: values [0, 100]
dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# Gravestone Doji: values [0, 100]
dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# Dark Cloud Cover: values [0, 100]
dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# Evening Doji Star: values [0, 100]
dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# Evening Star: values [0, 100]
dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
"""
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
"""
# Three Line Strike: values [0, -100, 100]
dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# Spinning Top: values [0, -100, 100]
dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# Engulfing: values [0, -100, 100]
dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# Harami: values [0, -100, 100]
dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# Three Outside Up/Down: values [0, -100, 100]
dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# Three Inside Up/Down: values [0, -100, 100]
dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
"""
# Chart type
# ------------------------------------
"""
# Heikinashi stategy
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['ha_open'] = heikinashi['open']
dataframe['ha_close'] = heikinashi['close']
dataframe['ha_high'] = heikinashi['high']
dataframe['ha_low'] = heikinashi['low']
"""
return dataframe
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
@ -91,6 +257,7 @@ class TestStrategy(IStrategy):
def hyperopt_space(self) -> List[Dict]:
"""
Define your Hyperopt space for the strategy
:return: Dict
"""
space = {
'adx': hp.choice('adx', [