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Matthias
2018-08-01 07:26:13 +02:00
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12
docs/backtesting.md
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@@ -29,25 +29,25 @@ The backtesting is very easy with freqtrade.
#### With 5 min tickers (Per default)
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation
python3 ./freqtrade/main.py backtesting
```
#### With 1 min tickers
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --ticker-interval 1m
python3 ./freqtrade/main.py backtesting --ticker-interval 1m
```
#### Update cached pairs with the latest data
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --refresh-pairs-cached
python3 ./freqtrade/main.py backtesting --refresh-pairs-cached
```
#### With live data (do not alter your testdata files)
```bash
python3 ./freqtrade/main.py backtesting --realistic-simulation --live
python3 ./freqtrade/main.py backtesting --live
```
#### Using a different on-disk ticker-data source
@@ -83,7 +83,7 @@ with filename.open() as file:
data = json.load(file)
columns = ["pair", "profit", "opents", "closets", "index", "duration",
"open_rate", "close_rate", "open_at_end"]
"open_rate", "close_rate", "open_at_end", "sell_reason"]
df = pd.DataFrame(data, columns=columns)
df['opents'] = pd.to_datetime(df['opents'],
@@ -98,6 +98,8 @@ df['closets'] = pd.to_datetime(df['closets'],
)
```
If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.
#### Exporting trades to file specifying a custom filename
```bash

41
docs/bot-optimization.md
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@@ -39,7 +39,6 @@ A strategy file contains all the information needed to build a good strategy:
- Sell strategy rules
- Minimal ROI recommended
- Stoploss recommended
- Hyperopt parameter
The bot also include a sample strategy called `TestStrategy` you can update: `user_data/strategies/test_strategy.py`.
You can test it with the parameter: `--strategy TestStrategy`
@@ -61,22 +60,22 @@ file as reference.**
### Buy strategy
Edit the method `populate_buy_trend()` into your strategy file to
update your 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(self, dataframe: DataFrame) -> DataFrame:
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['blower']) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
@@ -87,38 +86,47 @@ def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
### Sell strategy
Edit the method `populate_sell_trend()` into your strategy file to update your sell strategy.
Please note that the sell-signal is only used if `use_sell_signal` is set to true in the configuration.
Sample from `user_data/strategies/test_strategy.py`:
```python
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['blower']) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe
```
## Add more Indicator
## Add more 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.
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.
You should only add the indicators used in either `populate_buy_trend()`, `populate_sell_trend()`, or to populate another indicator, otherwise performance may suffer.
Sample:
```python
def populate_indicators(dataframe: DataFrame) -> DataFrame:
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
dataframe['sar'] = ta.SAR(dataframe)
dataframe['adx'] = ta.ADX(dataframe)
@@ -149,6 +157,11 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
return dataframe
```
### Metadata dict
The metadata-dict (available for `populate_buy_trend`, `populate_sell_trend`, `populate_indicators`) contains additional information.
Currently this is `pair`, which can be accessed using `metadata['pair']` - and will return a pair in the format `XRP/BTC`.
### Want more indicator examples
Look into the [user_data/strategies/test_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/test_strategy.py).

45
docs/bot-usage.md
View File

@@ -117,18 +117,21 @@ python3 ./freqtrade/main.py -c config.json --db-url sqlite:///tradesv3.dry_run.s
Backtesting also uses the config specified via `-c/--config`.
```
usage: main.py backtesting [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-l] [-r] [--export EXPORT]
[--export-filename EXPORTFILENAME]
usage: main.py backtesting [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
[--timerange TIMERANGE] [-l] [-r]
[--export EXPORT] [--export-filename PATH]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
specify ticker interval (1m, 5m, 30m, 1h, 1d)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking)
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number)
--timerange TIMERANGE
specify what timerange of data to use.
-l, --live using live data
@@ -138,11 +141,13 @@ optional arguments:
run your backtesting with up-to-date data.
--export EXPORT export backtest results, argument are: trades Example
--export=trades
--export-filename EXPORTFILENAME
--export-filename PATH
Save backtest results to this filename requires
--export to be set as well Example --export-
filename=backtest_today.json (default: backtest-
result.json
filename=user_data/backtest_data/backtest_today.json
(default: user_data/backtest_data/backtest-
result.json)
```
### How to use --refresh-pairs-cached parameter?
@@ -164,22 +169,28 @@ To optimize your strategy, you can use hyperopt parameter hyperoptimization
to find optimal parameter values for your stategy.
```
usage: main.py hyperopt [-h] [-i TICKER_INTERVAL] [--realistic-simulation]
[--timerange TIMERANGE] [-e INT]
[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
usage: freqtrade hyperopt [-h] [-i TICKER_INTERVAL] [--eps] [--dmmp]
[--timerange TIMERANGE] [-e INT]
[-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]]
optional arguments:
-h, --help show this help message and exit
-i TICKER_INTERVAL, --ticker-interval TICKER_INTERVAL
specify ticker interval (1m, 5m, 30m, 1h, 1d)
--realistic-simulation
uses max_open_trades from config to simulate real
world limitations
--timerange TIMERANGE specify what timerange of data to use.
--eps, --enable-position-stacking
Allow buying the same pair multiple times (position
stacking)
--dmmp, --disable-max-market-positions
Disable applying `max_open_trades` during backtest
(same as setting `max_open_trades` to a very high
number)
--timerange TIMERANGE
specify what timerange of data to use.
-e INT, --epochs INT specify number of epochs (default: 100)
-s {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...], --spaces {all,buy,roi,stoploss} [{all,buy,roi,stoploss} ...]
Specify which parameters to hyperopt. Space separate
list. Default: all
```
## A parameter missing in the configuration?

6
docs/configuration.md
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@@ -27,6 +27,7 @@ The table below will list all configuration parameters.
| `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.
| `trailing_stoploss` | false | No | Enables trailing stop-loss (based on `stoploss` in either configuration or strategy file).
| `trailing_stoploss_positve` | 0 | No | Changes stop-loss once profit has been reached.
| `trailing_stoploss_positve_offset` | 0 | No | Offset on when to apply `trailing_stoploss_positive`. Percentage value which should be positive.
| `unfilledtimeout.buy` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled buy order to complete, after which the order will be cancelled.
| `unfilledtimeout.sell` | 10 | Yes | How long (in minutes) the bot will wait for an unfilled sell 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.
@@ -41,6 +42,11 @@ The table below will list all configuration parameters.
| `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
| `telegram.token` | token | No | Your Telegram bot token. Only required if `telegram.enabled` is `true`.
| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required if `telegram.enabled` is `true`.
| `webhook.enabled` | false | No | Enable useage of Webhook notifications
| `webhook.url` | false | No | URL for the webhook. Only required if `webhook.enabled` is `true`. See the [webhook documentation](webhook-config.md) for more details.
| `webhook.webhookbuy` | false | No | Payload to send on buy. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhooksell` | false | No | Payload to send on sell. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `webhook.webhookstatus` | false | No | Payload to send on status calls. Only required if `webhook.enabled` is `true`. See the [webhook documentationV](webhook-config.md) for more details.
| `db_url` | `sqlite:///tradesv3.sqlite` | No | Declares database URL to use. NOTE: This defaults to `sqlite://` if `dry_run` is `True`.
| `initial_state` | running | No | Defines the initial application state. More information below.
| `strategy` | DefaultStrategy | No | Defines Strategy class to use.

2
docs/index.md
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@@ -27,8 +27,10 @@ Pull-request. Do not hesitate to reach us on
- [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
- [Control the bot with telegram](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md)
- [Receive notifications via webhook](https://github.com/freqtrade/freqtrade/blob/develop/docs/webhook-config.md)
- [Contribute to the project](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [How to contribute](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [Run tests & Check PEP8 compliance](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
- [FAQ](https://github.com/freqtrade/freqtrade/blob/develop/docs/faq.md)
- [SQL cheatsheet](https://github.com/freqtrade/freqtrade/blob/develop/docs/sql_cheatsheet.md)
- [Sandbox Testing](https://github.com/freqtrade/freqtrade/blob/develop/docs/sandbox-testing.md))

14
docs/installation.md
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@@ -56,23 +56,29 @@ Reset parameter will hard reset your branch (only if you are on `master` or `dev
Config parameter is a `config.json` configurator. This script will ask you questions to setup your bot and create your `config.json`.
## Manual installation - Linux/MacOS
The following steps are made for Linux/MacOS environment
**1. Clone the repo**
### 1. Clone the repo
```bash
git clone git@github.com:freqtrade/freqtrade.git
git checkout develop
cd freqtrade
```
**2. Create the config file**
### 2. Create the config file
Switch `"dry_run": true,`
```bash
cp config.json.example config.json
vi config.json
```
**3. Build your docker image and run it**
### 3. Build your docker image and run it
```bash
docker build -t freqtrade .
docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade

8
docs/plotting.md
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@@ -24,7 +24,7 @@ script/plot_dataframe.py [-h] [-p pair] [--live]
Example
```
python scripts/plot_dataframe.py -p BTC_ETH
python scripts/plot_dataframe.py -p BTC/ETH
```
The `-p` pair argument, can be used to specify what
@@ -34,18 +34,18 @@ pair you would like to plot.
To plot the current live price use the `--live` flag:
```
python scripts/plot_dataframe.py -p BTC_ETH --live
python scripts/plot_dataframe.py -p BTC/ETH --live
```
To plot a timerange (to zoom in):
```
python scripts/plot_dataframe.py -p BTC_ETH --timerange=100-200
python scripts/plot_dataframe.py -p BTC/ETH --timerange=100-200
```
Timerange doesn't work with live data.
To plot trades stored in a database use `--db-url` argument:
```
python scripts/plot_dataframe.py --db-url tradesv3.dry_run.sqlite -p BTC_ETH
python scripts/plot_dataframe.py --db-url sqlite:///tradesv3.dry_run.sqlite -p BTC/ETH
```
To plot a test strategy the strategy should have first be backtested.

151
docs/sandbox-testing.md Normal file
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@@ -0,0 +1,151 @@
# Sandbox API testing
Where an exchange provides a sandbox for risk-free integration, or end-to-end, testing CCXT provides access to these.
This document is a *light overview of configuring Freqtrade and GDAX sandbox.
This can be useful to developers and trader alike as Freqtrade is quite customisable.
When testing your API connectivity, make sure to use the following URLs.
***Website**
https://public.sandbox.gdax.com
***REST API**
https://api-public.sandbox.gdax.com
---
# Configure a Sandbox account on Gdax
Aim of this document section
- An sanbox account
- create 2FA (needed to create an API)
- Add test 50BTC to account
- Create :
- - API-KEY
- - API-Secret
- - API Password
## Acccount
This link will redirect to the sandbox main page to login / create account dialogues:
https://public.sandbox.pro.coinbase.com/orders/
After registration and Email confimation you wil be redirected into your sanbox account. It is easy to verify you're in sandbox by checking the URL bar.
> https://public.sandbox.pro.coinbase.com/
## Enable 2Fa (a prerequisite to creating sandbox API Keys)
From within sand box site select your profile, top right.
>Or as a direct link: https://public.sandbox.pro.coinbase.com/profile
From the menu panel to the left of the screen select
> Security: "*View or Update*"
In the new site select "enable authenticator" as typical google Authenticator.
- open Google Authenticator on your phone
- scan barcode
- enter your generated 2fa
## Enable API Access
From within sandbox select profile>api>create api-keys
>or as a direct link: https://public.sandbox.pro.coinbase.com/profile/api
Click on "create one" and ensure **view** and **trade** are "checked" and sumbit your 2Fa
- **Copy and paste the Passphase** into a notepade this will be needed later
- **Copy and paste the API Secret** popup into a notepad this will needed later
- **Copy and paste the API Key** into a notepad this will needed later
## Add 50 BTC test funds
To add funds, use the web interface deposit and withdraw buttons.
To begin select 'Wallets' from the top menu.
> Or as a direct link: https://public.sandbox.pro.coinbase.com/wallets
- Deposits (bottom left of screen)
- - Deposit Funds Bitcoin
- - - Coinbase BTC Wallet
- - - - Max (50 BTC)
- - - - - Deposit
*This process may be repeated for other currencies, ETH as example*
---
# Configure Freqtrade to use Gax Sandbox
The aim of this document section
- Enable sandbox URLs in Freqtrade
- Configure API
- - secret
- - key
- - passphrase
## Sandbox URLs
Freqtrade makes use of CCXT which in turn provides a list of URLs to Freqtrade.
These include `['test']` and `['api']`.
- `[Test]` if available will point to an Exchanges sandbox.
- `[Api]` normally used, and resolves to live API target on the exchange
To make use of sandbox / test add "sandbox": true, to your config.json
```
"exchange": {
"name": "gdax",
"sandbox": true,
"key": "5wowfxemogxeowo;heiohgmd",
"secret": "/ZMH1P62rCVmwefewrgcewX8nh4gob+lywxfwfxwwfxwfNsH1ySgvWCUR/w==",
"password": "1bkjfkhfhfu6sr",
"pair_whitelist": [
"BTC/USD"
```
Also insert your
- api-key (noted earlier)
- api-secret (noted earlier)
- password (the passphrase - noted earlier)
---
## You should now be ready to test your sandbox!
Ensure Freqtrade logs show the sandbox URL, and trades made are shown in sandbox.
** Typically the BTC/USD has the most activity in sandbox to test against.
## GDAX - Old Candles problem
It is my experience that GDAX sandbox candles may be 20+- minutes out of date. This can cause trades to fail as one of Freqtrades safety checks
To disable this check, edit:
>strategy/interface.py
Look for the following section:
```
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
```
You could Hash out the entire check as follows:
```
# # Check if dataframe is out of date
# signal_date = arrow.get(latest['date'])
# interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
# if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
# logger.warning(
# 'Outdated history for pair %s. Last tick is %s minutes old',
# pair,
# (arrow.utcnow() - signal_date).seconds // 60
# )
# return False, False
```
Or inrease the timeout to offer a level of protection/alignment of this test to freqtrade in live.
As example, to allow an additional 30 minutes. "(interval_minutes * 2 + 5 + 30)"
```
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5 + 30))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair,
(arrow.utcnow() - signal_date).seconds // 60
)
return False, False
```

2
docs/sql_cheatsheet.md
View File

@@ -59,7 +59,7 @@ SELECT * FROM trades;
```sql
UPDATE trades
SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate
SET is_open=0, close_date=<close_date>, close_rate=<close_rate>, close_profit=close_rate/open_rate-1
WHERE id=<trade_ID_to_update>;
```

13
docs/stoploss.md
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@@ -35,14 +35,17 @@ basically what this means is that your stop loss will be adjusted to be always b
### Custom positive loss
Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your buy turns positive,
the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you are in the
black, it will be changed to be only a 1% stop loss
Due to demand, it is possible to have a default stop loss, when you are in the red with your buy, but once your profit surpasses a certain percentage,
the system will utilize a new stop loss, which can be a different value. For example your default stop loss is 5%, but once you have 1.1% profit,
it will be changed to be only a 1% stop loss, which trails the green candles until it goes below them.
This can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
Both values can be configured in the main configuration file and requires `"trailing_stop": true` to be set to true.
``` json
"trailing_stop_positive": 0.01,
"trailing_stop_positive_offset": 0.011,
```
The 0.01 would translate to a 1% stop loss, once you hit profit.
The 0.01 would translate to a 1% stop loss, once you hit 1.1% profit.
You should also make sure to have this value higher than your minimal ROI, otherwise minimal ROI will apply first and sell your trade.

74
docs/webhook-config.md Normal file
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@@ -0,0 +1,74 @@
# Webhook usage
This page explains how to configure your bot to talk to webhooks.
## Configuration
Enable webhooks by adding a webhook-section to your configuration file, and setting `webhook.enabled` to `true`.
Sample configuration (tested using IFTTT).
```json
"webhook": {
"enabled": true,
"url": "https://maker.ifttt.com/trigger/<YOUREVENT>/with/key/<YOURKEY>/",
"webhookbuy": {
"value1": "Buying {pair}",
"value2": "limit {limit:8f}",
"value3": "{stake_amount:8f} {stake_currency}"
},
"webhooksell": {
"value1": "Selling {pair}",
"value2": "limit {limit:8f}",
"value3": "profit: {profit_amount:8f} {stake_currency}"
},
"webhookstatus": {
"value1": "Status: {status}",
"value2": "",
"value3": ""
}
},
```
The url in `webhook.url` should point to the correct url for your webhook. If you're using [IFTTT](https://ifttt.com) (as shown in the sample above) please insert our event and key to the url.
Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.
### Webhookbuy
The fields in `webhook.webhookbuy` are filled when the bot executes a buy. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* market_url
* limit
* stake_amount
* stake_amount_fiat
* stake_currency
* fiat_currency
### Webhooksell
The fields in `webhook.webhooksell` are filled when the bot sells a trade. Parameters are filled using string.format.
Possible parameters are:
* exchange
* pair
* gain
* market_url
* limit
* amount
* open_rate
* current_rate
* profit_amount
* profit_percent
* profit_fiat
* stake_currency
* fiat_currency
### Webhookstatus
The fields in `webhook.webhookstatus` are used for regular status messages (Started / Stopped / ...). Parameters are filled using string.format.
The only possible value here is `{status}`.