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4
.gitignore vendored
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@ -5,6 +5,8 @@ config.json
*.sqlite
.hyperopt
logfile.txt
hyperopt_trials.pickle
user_data/
# Byte-compiled / optimized / DLL files
__pycache__/
@ -85,5 +87,3 @@ target/
.venv
.idea
.vscode
hyperopt_trials.pickle

15
README.md
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@ -136,8 +136,8 @@ to understand the requirements before sending your pull-requests.
### Bot commands
```bash
usage: main.py [-h] [-c PATH] [-v] [--version] [--dynamic-whitelist [INT]]
[--dry-run-db]
usage: main.py [-h] [-v] [--version] [-c PATH] [--dry-run-db] [--datadir PATH]
[--dynamic-whitelist [INT]]
{backtesting,hyperopt} ...
Simple High Frequency Trading Bot for crypto currencies
@ -149,16 +149,17 @@ 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
--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)
--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.
--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)
```
More details on:
- [How to run the bot](https://github.com/gcarq/freqtrade/blob/develop/docs/bot-usage.md#bot-commands)

1
config.json.example
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@ -4,6 +4,7 @@
"stake_amount": 0.05,
"fiat_display_currency": "USD",
"dry_run": false,
"ticker_interval": 5,
"minimal_roi": {
"40": 0.0,
"30": 0.01,

44
docs/backtesting.md
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@ -14,7 +14,7 @@ real data. This is what we call
Backtesting will use the crypto-currencies (pair) from your config file
and load static tickers located in
[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
[/freqtrade/tests/testdata](https://github.com/gcarq/freqtrade/tree/develop/freqtrade/tests/testdata).
If the 5 min and 1 min ticker for the crypto-currencies to test is not
already in the `testdata` folder, backtesting will download them
automatically. Testdata files will not be updated until you specify it.
@ -51,15 +51,49 @@ python3 ./freqtrade/main.py backtesting --realistic-simulation --live
python3 ./freqtrade/main.py backtesting --datadir freqtrade/tests/testdata-20180101
```
**Exporting trades to file**
```bash
freqtrade backtesting --export trades
```
**Running backtest with smaller testset**
Use the `--timerange` argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
```bash
python3 ./freqtrade/main.py backtesting --timerange=-200
```
***Advanced use of timerange***
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.
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`
Incoming feature, not implemented yet:
- `--timerange=-20180131`
- `--timerange=20180101-`
- `--timerange=20180101-20181231`
**Update testdata directory**
To update your testdata directory, or download into another testdata directory:
```bash
mkdir freqtrade/tests/testdata-20180113
cp freqtrade/tests/testdata/pairs.json freqtrade/tests/testdata-20180113
cd freqtrade/tests/testdata-20180113
mkdir -p user_data/data/testdata-20180113
cp freqtrade/tests/testdata/pairs.json user_data/data-20180113
cd user_data/data-20180113
```
Possibly edit pairs.json file to include/exclude pairs
python download_backtest_data.py -p pairs.json
```bash
python freqtrade/tests/testdata/download_backtest_data.py -p pairs.json
```
The script will read your pairs.json file, and download ticker data

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 assume 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 indicators 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.

18
docs/configuration.md
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@ -17,10 +17,11 @@ The table below will list all configuration parameters.
| `max_open_trades` | 3 | Yes | Number of trades open your bot will have.
| `stake_currency` | BTC | Yes | Crypto-currency used for trading.
| `stake_amount` | 0.05 | Yes | Amount of crypto-currency your bot will use for each trade. Per default, the bot will use (0.05 BTC x 3) = 0.15 BTC in total will be always engaged.
| `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.
@ -29,9 +30,10 @@ The table below will list all configuration parameters.
| `exchange.pair_whitelist` | [] | No | List of currency to use by the bot. Can be overrided with `--dynamic-whitelist` param.
| `exchange.pair_blacklist` | [] | No | List of currency the bot must avoid. Useful when using `--dynamic-whitelist` param.
| `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`.
| `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
| `telegram.enabled` | true | Yes | Enable or not the usage of Telegram.
| `telegram.token` | token | No | Your Telegram bot token. Only required is `enable` is `true`.
| `telegram.chat_id` | chat_id | No | Your personal Telegram account id. Only required is `enable` is `true`.
| `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`.
| `initial_state` | running | No | Defines the initial application state. More information below.
| `internals.process_throttle_secs` | 5 | Yes | Set the process throttle. Value in second.
@ -51,11 +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`)

56
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 +163,18 @@ 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
Use the --timeperiod argument to change how much of the testset
you want to use. The last N ticks/timeframes will be used.
Example:
```bash
python3 ./freqtrade/main.py hyperopt --timeperiod -200
```
### Hyperopt with MongoDB
Hyperopt with MongoDB, is like Hyperopt under steroids. As you saw by
@ -260,15 +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)
```
@ -276,7 +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

48
docs/plotting.md Normal file
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@ -0,0 +1,48 @@
# Plotting
This page explains how to plot prices, indicator, profits.
## Table of Contents
- [Plot price and indicators](#plot-price-and-indicators)
- [Plot profit](#plot-profit)
## Plot price and indicators
Usage for the price plotter:
script/plot_dataframe.py [-h] [-p pair]
Example
```
python script/plot_dataframe.py -p BTC_ETH,BTC_LTC
```
The -p pair argument, can be used to specify what
pair you would like to plot.
## Plot profit
The profit plotter show a picture with three plots:
1) Average closing price for all pairs
2) The summarized profit made by backtesting.
Note that this is not the real-world profit, but
more of an estimate.
3) Each pair individually profit
The first graph is good to get a grip of how the overall market
progresses.
The second graph will show how you algorithm works or doesnt.
Perhaps you want an algorithm that steadily makes small profits,
or one that acts less seldom, but makes big swings.
The third graph can be useful to spot outliers, events in pairs
that makes profit spikes.
Usage for the profit plotter:
script/plot_profit.py [-h] [-p pair] [--datadir directory] [--ticker_interval num]
The -p pair argument, can be used to plot a single pair
Example
```
python python scripts/plot_profit.py --datadir ../freqtrade/freqtrade/tests/testdata-20171221/ -p BTC_LTC
```

2
freqtrade/__init__.py
View File

@ -1,5 +1,5 @@
""" FreqTrade bot """
__version__ = '0.15.1'
__version__ = '0.16.0'
class DependencyException(BaseException):

242
freqtrade/analyze.py
View File

@ -7,11 +7,10 @@ from enum import Enum
from typing import Dict, List
import arrow
import talib.abstract as ta
from pandas import DataFrame, to_datetime
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import get_ticker_history
from freqtrade.strategy.strategy import Strategy
logger = logging.getLogger(__name__)
@ -30,8 +29,9 @@ def parse_ticker_dataframe(ticker: list) -> DataFrame:
"""
columns = {'C': 'close', 'V': 'volume', 'O': 'open', 'H': 'high', 'L': 'low', 'T': 'date'}
frame = DataFrame(ticker) \
.drop('BV', 1) \
.rename(columns=columns)
if 'BV' in frame:
frame.drop('BV', 1, inplace=True)
frame['date'] = to_datetime(frame['date'], utc=True, infer_datetime_format=True)
frame.sort_values('date', inplace=True)
return frame
@ -45,181 +45,8 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
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.
"""
# 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)
"""
# 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['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
strategy = Strategy()
return strategy.populate_indicators(dataframe=dataframe)
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
@ -228,20 +55,8 @@ def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return dataframe
strategy = Strategy()
return strategy.populate_buy_trend(dataframe=dataframe)
def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
@ -250,21 +65,8 @@ def populate_sell_trend(dataframe: DataFrame) -> DataFrame:
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe
strategy = Strategy()
return strategy.populate_sell_trend(dataframe=dataframe)
def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
@ -280,36 +82,40 @@ def analyze_ticker(ticker_history: List[Dict]) -> DataFrame:
return dataframe
def get_signal(pair: str, signal: SignalType) -> bool:
# FIX: Maybe return False, if an error has occured,
# Otherwise we might mask an error as an non-signal-scenario
def get_signal(pair: str, interval: int) -> (bool, bool):
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format BTC_ANT or BTC-ANT
:return: True if pair is good for buying, False otherwise
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = get_ticker_history(pair)
ticker_hist = get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False
return (False, False) # return False ?
try:
dataframe = analyze_ticker(ticker_hist)
except ValueError as ex:
logger.warning('Unable to analyze ticker for pair %s: %s', pair, str(ex))
return False
return (False, False) # return False ?
except Exception as ex:
logger.exception('Unexpected error when analyzing ticker for pair %s: %s', pair, str(ex))
return False
return (False, False) # return False ?
if dataframe.empty:
return False
logger.warning('Empty dataframe for pair %s', pair)
return (False, False) # return False ?
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
if signal_date < arrow.now() - timedelta(minutes=10):
return False
logger.warning('Too old dataframe for pair %s', pair)
return (False, False) # return False ?
result = latest[signal.value] == 1
logger.debug('%s_trigger: %s (pair=%s, signal=%s)', signal.value, latest['date'], pair, result)
return result
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'], pair, str(buy), str(sell))
return (buy, sell)

2
freqtrade/exchange/__init__.py
View File

@ -139,7 +139,7 @@ def get_ticker(pair: str, refresh: Optional[bool] = True) -> dict:
@cached(TTLCache(maxsize=100, ttl=30))
def get_ticker_history(pair: str, tick_interval: Optional[int] = 5) -> List[Dict]:
def get_ticker_history(pair: str, tick_interval) -> List[Dict]:
return _API.get_ticker_history(pair, tick_interval)

11
freqtrade/exchange/bittrex.py
View File

@ -122,9 +122,10 @@ class Bittrex(Exchange):
raise OperationalException('{message} params=({pair})'.format(
message=data['message'],
pair=pair))
keys = ['Bid', 'Ask', 'Last']
if not data.get('result') or\
not all(key in data.get('result', {}) for key in ['Bid', 'Ask', 'Last']):
not all(key in data.get('result', {}) for key in keys) or\
not all(data.get('result', {})[key] is not None for key in keys):
raise ContentDecodingError('{message} params=({pair})'.format(
message='Got invalid response from bittrex',
pair=pair))
@ -141,6 +142,12 @@ class Bittrex(Exchange):
interval = 'oneMin'
elif tick_interval == 5:
interval = 'fiveMin'
elif tick_interval == 30:
interval = 'thirtyMin'
elif tick_interval == 60:
interval = 'hour'
elif tick_interval == 1440:
interval = 'Day'
else:
raise ValueError('Cannot parse tick_interval: {}'.format(tick_interval))

19
freqtrade/fiat_convert.py
View File

@ -48,7 +48,10 @@ class CryptoFiat():
return self._expiration - time.time() <= 0
class CryptoToFiatConverter():
class CryptoToFiatConverter(object):
__instance = None
_coinmarketcap = None
# Constants
SUPPORTED_FIAT = [
"AUD", "BRL", "CAD", "CHF", "CLP", "CNY", "CZK", "DKK",
@ -57,12 +60,16 @@ class CryptoToFiatConverter():
"RUB", "SEK", "SGD", "THB", "TRY", "TWD", "ZAR", "USD"
]
def __init__(self) -> None:
try:
self._coinmarketcap = Pymarketcap()
except BaseException:
self._coinmarketcap = None
def __new__(cls):
if CryptoToFiatConverter.__instance is None:
CryptoToFiatConverter.__instance = object.__new__(cls)
try:
CryptoToFiatConverter._coinmarketcap = Pymarketcap()
except BaseException:
CryptoToFiatConverter._coinmarketcap = None
return CryptoToFiatConverter.__instance
def __init__(self) -> None:
self._pairs = []
def convert_amount(self, crypto_amount: float, crypto_symbol: str, fiat_symbol: str) -> float:

238
freqtrade/main.py
View File

@ -14,11 +14,12 @@ from cachetools import cached, TTLCache
from freqtrade import (DependencyException, OperationalException, __version__,
exchange, persistence, rpc)
from freqtrade.analyze import SignalType, get_signal
from freqtrade.analyze import get_signal
from freqtrade.fiat_convert import CryptoToFiatConverter
from freqtrade.misc import (State, get_state, load_config, parse_args,
throttle, update_state)
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy import Strategy
logger = logging.getLogger('freqtrade')
@ -54,12 +55,49 @@ def refresh_whitelist(whitelist: List[str]) -> List[str]:
return final_list
def _process(nb_assets: Optional[int] = 0) -> bool:
def process_maybe_execute_buy(conf, interval):
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if create_trade(float(_CONF['stake_amount']), interval):
return True
else:
logger.info(
'Checked all whitelisted currencies. '
'Found no suitable entry positions for buying. Will keep looking ...'
)
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(trade, interval):
"""
Tries to execute a sell trade
:return: True if executed
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Got open order for %s', trade)
trade.update(exchange.get_order(trade.open_order_id))
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return handle_trade(trade, interval)
return False
def _process(interval: int, nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if a trade has been created or closed, False otherwise
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
@ -77,33 +115,16 @@ def _process(nb_assets: Optional[int] = 0) -> bool:
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
if len(trades) < _CONF['max_open_trades']:
try:
# Create entity and execute trade
state_changed = create_trade(float(_CONF['stake_amount']))
if not state_changed:
logger.info(
'Checked all whitelisted currencies. '
'Found no suitable entry positions for buying. Will keep looking ...'
)
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
state_changed = process_maybe_execute_buy(_CONF, interval)
for trade in trades:
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Got open order for %s', trade)
trade.update(exchange.get_order(trade.open_order_id))
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
state_changed = handle_trade(trade) or state_changed
state_changed |= process_maybe_execute_sell(trade, interval)
if 'unfilledtimeout' in _CONF:
# Check and handle any timed out open orders
check_handle_timedout(_CONF['unfilledtimeout'])
Trade.session.flush()
except (requests.exceptions.RequestException, json.JSONDecodeError) as error:
logger.warning(
'Got %s in _process(), retrying in 30 seconds...',
@ -120,6 +141,59 @@ def _process(nb_assets: Optional[int] = 0) -> bool:
return state_changed
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
# handle_timedout_limit_sell()?
def handle_timedout_limit_buy(trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
:return: True if order was fully cancelled
"""
exchange.cancel_order(trade.open_order_id)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
# FIX? do we really need to flush, caller of
# check_handle_timedout will flush afterwards
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
rpc.send_msg('*Timeout:* Unfilled buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return True
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
rpc.send_msg('*Timeout:* Remaining buy order for {} cancelled'.format(
trade.pair.replace('_', '/')))
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
def handle_timedout_limit_sell(trade: Trade, order: Dict) -> bool:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
exchange.cancel_order(trade.open_order_id)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
rpc.send_msg('*Timeout:* Unfilled sell order for {} cancelled'.format(
trade.pair.replace('_', '/')))
logger.info('Sell order timeout for %s.', trade)
return True
else:
# TODO: figure out how to handle partially complete sell orders
return False
def check_handle_timedout(timeoutvalue: int) -> None:
"""
Check if any orders are timed out and cancel if neccessary
@ -129,39 +203,21 @@ def check_handle_timedout(timeoutvalue: int) -> None:
timeoutthreashold = arrow.utcnow().shift(minutes=-timeoutvalue).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
order = exchange.get_order(trade.open_order_id)
try:
order = exchange.get_order(trade.open_order_id)
except requests.exceptions.RequestException:
logger.info('Cannot query order for %s due to %s', trade, traceback.format_exc())
continue
ordertime = arrow.get(order['opened'])
# Check if trade is still actually open
if int(order['remaining']) == 0:
continue
if order['type'] == "LIMIT_BUY" and ordertime < timeoutthreashold:
# Buy timeout - cancel order
exchange.cancel_order(trade.open_order_id)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
else:
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
handle_timedout_limit_buy(trade, order)
elif order['type'] == "LIMIT_SELL" and ordertime < timeoutthreashold:
# Sell timeout - cancel order and update trade
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
exchange.cancel_order(trade.open_order_id)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
logger.info('Sell order timeout for %s.', trade)
return True
else:
# TODO: figure out how to handle partially complete sell orders
pass
handle_timedout_limit_sell(trade, order)
def execute_sell(trade: Trade, limit: float) -> None:
@ -177,12 +233,25 @@ def execute_sell(trade: Trade, limit: float) -> None:
fmt_exp_profit = round(trade.calc_profit_percent(rate=limit) * 100, 2)
profit_trade = trade.calc_profit(rate=limit)
current_rate = exchange.get_ticker(trade.pair, False)['bid']
profit = trade.calc_profit_percent(current_rate)
message = '*{exchange}:* Selling [{pair}]({pair_url}) with limit `{limit:.8f}`'.format(
message = """*{exchange}:* Selling
*Current Pair:* [{pair}]({pair_url})
*Limit:* `{limit}`
*Amount:* `{amount}`
*Open Rate:* `{open_rate:.8f}`
*Current Rate:* `{current_rate:.8f}`
*Profit:* `{profit:.2f}%`
""".format(
exchange=trade.exchange,
pair=trade.pair.replace('_', '/'),
pair=trade.pair,
pair_url=exchange.get_pair_detail_url(trade.pair),
limit=limit
limit=limit,
open_rate=trade.open_rate,
current_rate=current_rate,
amount=round(trade.amount, 8),
profit=round(profit * 100, 2),
)
# For regular case, when the configuration exists
@ -221,14 +290,16 @@ def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -
Based an earlier trade and current price and ROI configuration, decides whether bot should sell
:return True if bot should sell at current rate
"""
strategy = Strategy()
current_profit = trade.calc_profit_percent(current_rate)
if 'stoploss' in _CONF and current_profit < float(_CONF['stoploss']):
if strategy.stoploss is not None and current_profit < float(strategy.stoploss):
logger.debug('Stop loss hit.')
return True
# Check if time matches and current rate is above threshold
time_diff = (current_time - trade.open_date).total_seconds() / 60
for duration, threshold in sorted(_CONF['minimal_roi'].items()):
for duration, threshold in sorted(strategy.minimal_roi.items()):
if time_diff > float(duration) and current_profit > threshold:
return True
@ -236,7 +307,7 @@ def min_roi_reached(trade: Trade, current_rate: float, current_time: datetime) -
return False
def handle_trade(trade: Trade) -> bool:
def handle_trade(trade: Trade, interval: int) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
@ -247,24 +318,27 @@ def handle_trade(trade: Trade) -> bool:
logger.debug('Handling %s ...', trade)
current_rate = exchange.get_ticker(trade.pair)['bid']
# Check if minimal roi has been reached
if min_roi_reached(trade, current_rate, datetime.utcnow()):
(buy, sell) = (False, False)
if _CONF.get('experimental', {}).get('use_sell_signal'):
(buy, sell) = get_signal(trade.pair, interval)
# Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
if not buy and min_roi_reached(trade, current_rate, datetime.utcnow()):
logger.debug('Executing sell due to ROI ...')
execute_sell(trade, current_rate)
return True
# Experimental: Check if sell signal has been enabled and triggered
if _CONF.get('experimental', {}).get('use_sell_signal'):
# Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
if _CONF.get('experimental', {}).get('sell_profit_only'):
logger.debug('Checking if trade is profitable ...')
if trade.calc_profit(rate=current_rate) <= 0:
return False
logger.debug('Checking sell_signal ...')
if get_signal(trade.pair, SignalType.SELL):
logger.debug('Executing sell due to sell signal ...')
execute_sell(trade, current_rate)
return True
# Experimental: Check if the trade is profitable before selling it (avoid selling at loss)
if _CONF.get('experimental', {}).get('sell_profit_only', False):
logger.debug('Checking if trade is profitable ...')
if not buy and trade.calc_profit(rate=current_rate) <= 0:
return False
if sell and not buy:
logger.debug('Executing sell due to sell signal ...')
execute_sell(trade, current_rate)
return True
return False
@ -277,7 +351,7 @@ def get_target_bid(ticker: Dict[str, float]) -> float:
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def create_trade(stake_amount: float) -> bool:
def create_trade(stake_amount: float, interval: int) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
@ -305,7 +379,8 @@ def create_trade(stake_amount: float) -> bool:
# Pick pair based on StochRSI buy signals
for _pair in whitelist:
if get_signal(_pair, SignalType.BUY):
(buy, sell) = get_signal(_pair, interval)
if buy and not sell:
pair = _pair
break
else:
@ -360,6 +435,9 @@ def init(config: dict, db_url: Optional[str] = None) -> None:
persistence.init(config, db_url)
exchange.init(config)
strategy = Strategy()
strategy.init(config)
# Set initial application state
initial_state = config.get('initial_state')
if initial_state:
@ -398,7 +476,7 @@ def cleanup() -> None:
exit(0)
def main(sysargv=sys.argv[1:]) -> None:
def main(sysargv=sys.argv[1:]) -> int:
"""
Loads and validates the config and handles the main loop
:return: None
@ -410,7 +488,7 @@ def main(sysargv=sys.argv[1:]) -> None:
# A subcommand has been issued
if hasattr(args, 'func'):
args.func(args)
exit(0)
return 0
# Initialize logger
logging.basicConfig(
@ -427,6 +505,9 @@ def main(sysargv=sys.argv[1:]) -> None:
# Load and validate configuration
_CONF = load_config(args.config)
# Add the strategy file to use
_CONF.update({'strategy': args.strategy})
# Initialize all modules and start main loop
if args.dynamic_whitelist:
logger.info('Using dynamically generated whitelist. (--dynamic-whitelist detected)')
@ -444,6 +525,7 @@ def main(sysargv=sys.argv[1:]) -> None:
try:
init(_CONF)
old_state = None
while True:
new_state = get_state()
# Log state transition
@ -458,6 +540,7 @@ def main(sysargv=sys.argv[1:]) -> None:
_process,
min_secs=_CONF['internals'].get('process_throttle_secs', 10),
nb_assets=args.dynamic_whitelist,
interval=int(_CONF.get('ticker_interval', 5))
)
old_state = new_state
except KeyboardInterrupt:
@ -466,7 +549,8 @@ def main(sysargv=sys.argv[1:]) -> None:
logger.exception('Got fatal exception!')
finally:
cleanup()
return 0
if __name__ == '__main__':
main()
main(sys.argv[1:])

188
freqtrade/misc.py
View File

@ -4,6 +4,7 @@ import json
import logging
import time
import os
import re
from typing import Any, Callable, Dict, List
from jsonschema import Draft4Validator, validate
@ -115,6 +116,22 @@ def common_args_parser(description: str):
type=str,
metavar='PATH',
)
parser.add_argument(
'--datadir',
help='path to backtest data (default freqdata/tests/testdata)',
dest='datadir',
default=os.path.join('freqtrade', 'tests', 'testdata'),
type=str,
metavar='PATH',
)
parser.add_argument(
'-s', '--strategy',
help='specify strategy file (default: freqtrade/strategy/default_strategy.py)',
dest='strategy',
default='.default_strategy',
type=str,
metavar='PATH',
)
return parser
@ -131,14 +148,6 @@ def parse_args(args: List[str], description: str):
action='store_true',
dest='dry_run_db',
)
parser.add_argument(
'-dd', '--datadir',
help='path to backtest data (default freqdata/tests/testdata',
dest='datadir',
default=os.path.join('freqtrade', 'tests', 'testdata'),
type=str,
metavar='PATH',
)
parser.add_argument(
'--dynamic-whitelist',
help='dynamically generate and update whitelist \
@ -154,6 +163,113 @@ def parse_args(args: List[str], description: str):
return parser.parse_args(args)
def backtesting_options(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
'-l', '--live',
action='store_true',
dest='live',
help='using live data',
)
parser.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (1, 5, 30, 60, 1440)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
parser.add_argument(
'--realistic-simulation',
help='uses max_open_trades from config to simulate real world limitations',
action='store_true',
dest='realistic_simulation',
)
parser.add_argument(
'-r', '--refresh-pairs-cached',
help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
Use it if you want to run your backtesting with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
parser.add_argument(
'--export',
help='Export backtest results, argument are: trades\
Example --export=trades',
type=str,
default=None,
dest='export',
)
parser.add_argument(
'--timerange',
help='Specify what timerange of data to use.',
default=None,
type=str,
dest='timerange',
)
def hyperopt_options(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
'-e', '--epochs',
help='specify number of epochs (default: 100)',
dest='epochs',
default=100,
type=int,
metavar='INT',
)
parser.add_argument(
'--use-mongodb',
help='parallelize evaluations with mongodb (requires mongod in PATH)',
dest='mongodb',
action='store_true',
)
parser.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (default: 5)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
parser.add_argument(
'--timerange',
help='Specify what timerange of data to use.',
default=None,
type=str,
dest='timerange',
)
def parse_timerange(text):
if text is None:
return None
syntax = [('^-(\d{8})$', (None, 'date')),
('^(\d{8})-$', ('date', None)),
('^(\d{8})-(\d{8})$', ('date', 'date')),
('^(-\d+)$', (None, 'line')),
('^(\d+)-$', ('line', None)),
('^(\d+)-(\d+)$', ('index', 'index'))]
for rex, stype in syntax:
# Apply the regular expression to text
m = re.match(rex, text)
if m: # Regex has matched
rvals = m.groups()
n = 0
start = None
stop = None
if stype[0]:
start = rvals[n]
if stype[0] != 'date':
start = int(start)
n += 1
if stype[1]:
stop = rvals[n]
if stype[1] != 'date':
stop = int(stop)
return (stype, start, stop)
raise Exception('Incorrect syntax for timerange "%s"' % text)
def build_subcommands(parser: argparse.ArgumentParser) -> None:
""" Builds and attaches all subcommands """
from freqtrade.optimize import backtesting, hyperopt
@ -163,59 +279,12 @@ def build_subcommands(parser: argparse.ArgumentParser) -> None:
# Add backtesting subcommand
backtesting_cmd = subparsers.add_parser('backtesting', help='backtesting module')
backtesting_cmd.set_defaults(func=backtesting.start)
backtesting_cmd.add_argument(
'-l', '--live',
action='store_true',
dest='live',
help='using live data',
)
backtesting_cmd.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (default: 5)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
backtesting_cmd.add_argument(
'--realistic-simulation',
help='uses max_open_trades from config to simulate real world limitations',
action='store_true',
dest='realistic_simulation',
)
backtesting_cmd.add_argument(
'-r', '--refresh-pairs-cached',
help='refresh the pairs files in tests/testdata with the latest data from Bittrex. \
Use it if you want to run your backtesting with up-to-date data.',
action='store_true',
dest='refresh_pairs',
)
backtesting_options(backtesting_cmd)
# Add hyperopt subcommand
hyperopt_cmd = subparsers.add_parser('hyperopt', help='hyperopt module')
hyperopt_cmd.set_defaults(func=hyperopt.start)
hyperopt_cmd.add_argument(
'-e', '--epochs',
help='specify number of epochs (default: 100)',
dest='epochs',
default=100,
type=int,
metavar='INT',
)
hyperopt_cmd.add_argument(
'--use-mongodb',
help='parallelize evaluations with mongodb (requires mongod in PATH)',
dest='mongodb',
action='store_true',
)
hyperopt_cmd.add_argument(
'-i', '--ticker-interval',
help='specify ticker interval in minutes (default: 5)',
dest='ticker_interval',
default=5,
type=int,
metavar='INT',
)
hyperopt_options(hyperopt_cmd)
# Required json-schema for user specified config
@ -223,6 +292,7 @@ CONF_SCHEMA = {
'type': 'object',
'properties': {
'max_open_trades': {'type': 'integer', 'minimum': 1},
'ticker_interval': {'type': 'integer', 'enum': [1, 5, 30, 60, 1440]},
'stake_currency': {'type': 'string', 'enum': ['BTC', 'ETH', 'USDT']},
'stake_amount': {'type': 'number', 'minimum': 0.0005},
'fiat_display_currency': {'type': 'string', 'enum': ['AUD', 'BRL', 'CAD', 'CHF',
@ -276,7 +346,8 @@ CONF_SCHEMA = {
'internals': {
'type': 'object',
'properties': {
'process_throttle_secs': {'type': 'number'}
'process_throttle_secs': {'type': 'number'},
'interval': {'type': 'integer'}
}
}
},
@ -316,7 +387,6 @@ CONF_SCHEMA = {
'stake_amount',
'fiat_display_currency',
'dry_run',
'minimal_roi',
'bid_strategy',
'telegram'
]

55
freqtrade/optimize/__init__.py
View File

@ -6,13 +6,28 @@ import os
from typing import Optional, List, Dict
from pandas import DataFrame
from freqtrade.exchange import get_ticker_history
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.analyze import populate_indicators, parse_ticker_dataframe
from freqtrade import misc
from user_data.hyperopt_conf import hyperopt_optimize_conf
logger = logging.getLogger(__name__)
def load_tickerdata_file(datadir, pair, ticker_interval):
def trim_tickerlist(tickerlist, timerange):
(stype, start, stop) = timerange
if stype == (None, 'line'):
return tickerlist[stop:]
elif stype == ('line', None):
return tickerlist[0:start]
elif stype == ('index', 'index'):
return tickerlist[start:stop]
else:
return tickerlist
def load_tickerdata_file(datadir, pair, ticker_interval,
timerange=None):
"""
Load a pair from file,
:return dict OR empty if unsuccesful
@ -30,11 +45,13 @@ def load_tickerdata_file(datadir, pair, ticker_interval):
# Read the file, load the json
with open(file) as tickerdata:
pairdata = json.load(tickerdata)
if timerange:
pairdata = trim_tickerlist(pairdata, timerange)
return pairdata
def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]] = None,
refresh_pairs: Optional[bool] = False) -> Dict[str, List]:
def load_data(datadir: str, ticker_interval: int, pairs: Optional[List[str]] = None,
refresh_pairs: Optional[bool] = False, timerange=None) -> Dict[str, List]:
"""
Loads ticker history data for the given parameters
:param ticker_interval: ticker interval in minutes
@ -48,19 +65,24 @@ def load_data(datadir: str, ticker_interval: int = 5, pairs: Optional[List[str]]
# If the user force the refresh of pairs
if refresh_pairs:
logger.info('Download data for all pairs and store them in %s', datadir)
download_pairs(datadir, _pairs)
download_pairs(datadir, _pairs, ticker_interval)
for pair in _pairs:
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
if not pairdata:
# download the tickerdata from exchange
download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
# and retry reading the pair
pairdata = load_tickerdata_file(datadir, pair, ticker_interval)
pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
result[pair] = pairdata
return result
def tickerdata_to_dataframe(data):
preprocessed = preprocess(data)
return preprocessed
def preprocess(tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
"""Creates a dataframe and populates indicators for given ticker data"""
return {pair: populate_indicators(parse_ticker_dataframe(pair_data))
@ -73,21 +95,26 @@ def make_testdata_path(datadir: str) -> str:
'..', 'tests', 'testdata'))
def download_pairs(datadir, pairs: List[str]) -> bool:
"""For each pairs passed in parameters, download 1 and 5 ticker intervals"""
def download_pairs(datadir, pairs: List[str], ticker_interval: int) -> bool:
"""For each pairs passed in parameters, download the ticker intervals"""
for pair in pairs:
try:
for interval in [1, 5]:
download_backtesting_testdata(datadir, pair=pair, interval=interval)
download_backtesting_testdata(datadir, pair=pair, interval=ticker_interval)
except BaseException:
logger.info('Failed to download the pair: "{pair}", Interval: {interval} min'.format(
pair=pair,
interval=interval,
interval=ticker_interval,
))
return False
return True
def file_dump_json(filename, data):
with open(filename, "wt") as fp:
json.dump(data, fp)
# FIX: 20180110, suggest rename interval to tick_interval
def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) -> bool:
"""
Download the latest 1 and 5 ticker intervals from Bittrex for the pairs passed in parameters
@ -107,7 +134,6 @@ def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) ->
pair=filepair,
interval=interval,
))
filename = filename.replace('USDT_BTC', 'BTC_FAKEBULL')
if os.path.isfile(filename):
with open(filename, "rt") as fp:
@ -127,7 +153,6 @@ def download_backtesting_testdata(datadir: str, pair: str, interval: int = 5) ->
logger.debug("New End: {}".format(data[-1:][0]['T']))
data = sorted(data, key=lambda data: data['T'])
with open(filename, "wt") as fp:
json.dump(data, fp)
misc.file_dump_json(filename, data)
return True

154
freqtrade/optimize/backtesting.py
View File

@ -13,8 +13,8 @@ from freqtrade import exchange
from freqtrade.analyze import populate_buy_trend, populate_sell_trend
from freqtrade.exchange import Bittrex
from freqtrade.main import min_roi_reached
from freqtrade.optimize import preprocess
from freqtrade.persistence import Trade
from freqtrade.strategy.strategy import Strategy
logger = logging.getLogger(__name__)
@ -67,17 +67,60 @@ def generate_text_table(
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
def backtest(stake_amount: float, processed: Dict[str, DataFrame],
max_open_trades: int = 0, realistic: bool = True, sell_profit_only: bool = False,
stoploss: int = -1.00, use_sell_signal: bool = False) -> DataFrame:
def get_trade_entry(pair, row, ticker, trade_count_lock, args):
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
sell_profit_only = args.get('sell_profit_only', False)
stoploss = args.get('stoploss', -1)
use_sell_signal = args.get('use_sell_signal', False)
trade = Trade(open_rate=row.close,
open_date=row.date,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
for row2 in sell_subset.itertuples(index=True):
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
current_profit_percent = trade.calc_profit_percent(rate=row2.close)
if (sell_profit_only and current_profit_percent < 0):
continue
if min_roi_reached(trade, row2.close, row2.date) or \
(row2.sell == 1 and use_sell_signal) or \
current_profit_percent <= stoploss:
current_profit_btc = trade.calc_profit(rate=row2.close)
return row2, (pair,
current_profit_percent,
current_profit_btc,
row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
)
def backtest(args) -> DataFrame:
"""
Implements backtesting functionality
:param stake_amount: btc amount to use for each trade
:param processed: a processed dictionary with format {pair, data}
:param max_open_trades: maximum number of concurrent trades (default: 0, disabled)
:param realistic: do we try to simulate realistic trades? (default: True)
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
sell_profit_only: sell if profit only
use_sell_signal: act on sell-signal
stoploss: use stoploss
:return: DataFrame
"""
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', True)
record = args.get('record', None)
records = []
trades = []
trade_count_lock: dict = {}
exchange._API = Bittrex({'key': '', 'secret': ''})
@ -100,41 +143,25 @@ def backtest(stake_amount: float, processed: Dict[str, DataFrame],
# Increase lock
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade = Trade(
open_rate=row.close,
open_date=row.date,
stake_amount=stake_amount,
amount=stake_amount / row.open,
fee=exchange.get_fee()
)
# calculate win/lose forwards from buy point
sell_subset = ticker[row.Index + 1:][['close', 'date', 'sell']]
for row2 in sell_subset.itertuples(index=True):
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
current_profit_percent = trade.calc_profit_percent(rate=row2.close)
if (sell_profit_only and current_profit_percent < 0):
continue
if min_roi_reached(trade, row2.close, row2.date) or \
(row2.sell == 1 and use_sell_signal) or \
current_profit_percent <= stoploss:
current_profit_btc = trade.calc_profit(rate=row2.close)
lock_pair_until = row2.Index
trades.append(
(
pair,
current_profit_percent,
current_profit_btc,
row2.Index - row.Index,
current_profit_btc > 0,
current_profit_btc < 0
)
)
break
ret = get_trade_entry(pair, row, ticker,
trade_count_lock, args)
if ret:
row2, trade_entry = ret
lock_pair_until = row2.Index
trades.append(trade_entry)
if record:
# Note, need to be json.dump friendly
# record a tuple of pair, current_profit_percent,
# entry-date, duration
records.append((pair, trade_entry[1],
row.date.strftime('%s'),
row2.date.strftime('%s'),
row.Index, trade_entry[3]))
# For now export inside backtest(), maybe change so that backtest()
# returns a tuple like: (dataframe, records, logs, etc)
if record and record.find('trades') >= 0:
logger.info('Dumping backtest results')
misc.file_dump_json('backtest-result.json', records)
labels = ['currency', 'profit_percent', 'profit_BTC', 'duration', 'profit', 'loss']
return DataFrame.from_records(trades, columns=labels)
@ -150,47 +177,54 @@ def start(args):
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
ticker_interval = config.get('ticker_interval', args.ticker_interval)
logger.info('Using ticker_interval: %s ...', ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
data[pair] = exchange.get_ticker_history(pair, ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
timerange = misc.parse_timerange(args.timerange)
data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs,
timerange=timerange)
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# init the strategy to use
config.update({'strategy': args.strategy})
strategy = Strategy()
strategy.init(config)
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
preprocessed = optimize.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
)
sell_profit_only = config.get('experimental', {}).get('sell_profit_only', False)
use_sell_signal = config.get('experimental', {}).get('use_sell_signal', False)
results = backtest({'stake_amount': config['stake_amount'],
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': args.realistic_simulation,
'sell_profit_only': sell_profit_only,
'use_sell_signal': use_sell_signal,
'stoploss': strategy.stoploss,
'record': args.export
})
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], args.ticker_interval)

433
freqtrade/optimize/hyperopt.py
View File

@ -3,25 +3,31 @@
import json
import logging
import sys
import os
import pickle
import signal
import os
import sys
from functools import reduce
from math import exp
from operator import itemgetter
from typing import Dict, List
import numpy
import talib.abstract as ta
from hyperopt import STATUS_FAIL, STATUS_OK, Trials, fmin, hp, space_eval, tpe
from hyperopt.mongoexp import MongoTrials
from pandas import DataFrame
from freqtrade import main # noqa
from freqtrade import exchange, optimize
import freqtrade.vendor.qtpylib.indicators as qtpylib
# Monkey patch config
from freqtrade import main # noqa; noqa
from freqtrade import exchange, misc, optimize
from freqtrade.exchange import Bittrex
from freqtrade.misc import load_config
from freqtrade.optimize import backtesting
from freqtrade.optimize.backtesting import backtest
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from freqtrade.vendor.qtpylib.indicators import crossed_above
from freqtrade.strategy.strategy import Strategy
from user_data.hyperopt_conf import hyperopt_optimize_conf
# Remove noisy log messages
logging.getLogger('hyperopt.mongoexp').setLevel(logging.WARNING)
@ -49,63 +55,130 @@ PROCESSED = None # optimize.preprocess(optimize.load_data())
OPTIMIZE_CONFIG = hyperopt_optimize_conf()
# Hyperopt Trials
TRIALS_FILE = os.path.join('freqtrade', 'optimize', 'hyperopt_trials.pickle')
TRIALS_FILE = os.path.join('user_data', 'hyperopt_trials.pickle')
TRIALS = Trials()
# Monkey patch config
from freqtrade import main # noqa
main._CONF = OPTIMIZE_CONFIG
SPACE = {
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema5_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'stochf_cross'},
{'type': 'ht_sine'},
]),
'stoploss': hp.uniform('stoploss', -0.5, -0.02),
}
def populate_indicators(dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
"""
dataframe['adx'] = ta.ADX(dataframe)
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
dataframe['cci'] = ta.CCI(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['roc'] = ta.ROC(dataframe)
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']
# 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 Parabolic
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)
# 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 save_trials(trials, trials_path=TRIALS_FILE):
@ -152,13 +225,183 @@ def calculate_loss(total_profit: float, trade_count: int, trade_duration: float)
return trade_loss + profit_loss + duration_loss
def generate_roi_table(params):
roi_table = {}
roi_table["0"] = params['roi_p1'] + params['roi_p2'] + params['roi_p3']
roi_table[str(params['roi_t3'])] = params['roi_p1'] + params['roi_p2']
roi_table[str(params['roi_t3'] + params['roi_t2'])] = params['roi_p1']
roi_table[str(params['roi_t3'] + params['roi_t2'] + params['roi_t1'])] = 0
return roi_table
def roi_space() -> List[Dict]:
return {
'roi_t1': hp.quniform('roi_t1', 10, 220, 10),
'roi_t2': hp.quniform('roi_t2', 10, 120, 10),
'roi_t3': hp.quniform('roi_t3', 10, 120, 10),
'roi_p1': hp.quniform('roi_p1', 0.01, 0.05, 0.01),
'roi_p2': hp.quniform('roi_p2', 0.01, 0.10, 0.01),
'roi_p3': hp.quniform('roi_p3', 0.01, 0.30, 0.01),
}
def stoploss_space() -> Dict:
return {
'stoploss': hp.uniform('stoploss', -0.5, -0.02),
}
def indicator_space() -> List[Dict]:
"""
Define your Hyperopt space for searching strategy parameters
"""
return {
'macd_below_zero': hp.choice('macd_below_zero', [
{'enabled': False},
{'enabled': True}
]),
'mfi': hp.choice('mfi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
]),
'fastd': hp.choice('fastd', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('fastd-value', 10, 50, 1)}
]),
'adx': hp.choice('adx', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('adx-value', 15, 50, 1)}
]),
'rsi': hp.choice('rsi', [
{'enabled': False},
{'enabled': True, 'value': hp.quniform('rsi-value', 20, 40, 1)}
]),
'uptrend_long_ema': hp.choice('uptrend_long_ema', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_short_ema': hp.choice('uptrend_short_ema', [
{'enabled': False},
{'enabled': True}
]),
'over_sar': hp.choice('over_sar', [
{'enabled': False},
{'enabled': True}
]),
'green_candle': hp.choice('green_candle', [
{'enabled': False},
{'enabled': True}
]),
'uptrend_sma': hp.choice('uptrend_sma', [
{'enabled': False},
{'enabled': True}
]),
'trigger': hp.choice('trigger', [
{'type': 'lower_bb'},
{'type': 'lower_bb_tema'},
{'type': 'faststoch10'},
{'type': 'ao_cross_zero'},
{'type': 'ema3_cross_ema10'},
{'type': 'macd_cross_signal'},
{'type': 'sar_reversal'},
{'type': 'ht_sine'},
{'type': 'heiken_reversal_bull'},
{'type': 'di_cross'},
]),
}
def hyperopt_space() -> List[Dict]:
return {**indicator_space(), **roi_space(), **stoploss_space()}
def buy_strategy_generator(params) -> None:
"""
Define the buy strategy parameters to be used by hyperopt
"""
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if 'uptrend_long_ema' in params and params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100'])
if 'macd_below_zero' in params and params['macd_below_zero']['enabled']:
conditions.append(dataframe['macd'] < 0)
if 'uptrend_short_ema' in params and params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10'])
if 'mfi' in params and params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if 'fastd' in params and params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if 'adx' in params and params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if 'rsi' in params and params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if 'over_sar' in params and params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
if 'green_candle' in params and params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
if 'uptrend_sma' in params and params['uptrend_sma']['enabled']:
prevsma = dataframe['sma'].shift(1)
conditions.append(dataframe['sma'] > prevsma)
# TRIGGERS
triggers = {
'lower_bb': (
dataframe['close'] < dataframe['bb_lowerband']
),
'lower_bb_tema': (
dataframe['tema'] < dataframe['bb_lowerband']
),
'faststoch10': (qtpylib.crossed_above(
dataframe['fastd'], 10.0
)),
'ao_cross_zero': (qtpylib.crossed_above(
dataframe['ao'], 0.0
)),
'ema3_cross_ema10': (qtpylib.crossed_above(
dataframe['ema3'], dataframe['ema10']
)),
'macd_cross_signal': (qtpylib.crossed_above(
dataframe['macd'], dataframe['macdsignal']
)),
'sar_reversal': (qtpylib.crossed_above(
dataframe['close'], dataframe['sar']
)),
'ht_sine': (qtpylib.crossed_above(
dataframe['htleadsine'], dataframe['htsine']
)),
'heiken_reversal_bull': (
(qtpylib.crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
(dataframe['ha_low'] == dataframe['ha_open'])
),
'di_cross': (qtpylib.crossed_above(
dataframe['plus_di'], dataframe['minus_di']
)),
}
conditions.append(triggers.get(params['trigger']['type']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def optimizer(params):
global _CURRENT_TRIES
from freqtrade.optimize import backtesting
if 'roi_t1' in params:
strategy = Strategy()
strategy.minimal_roi = generate_roi_table(params)
backtesting.populate_buy_trend = buy_strategy_generator(params)
results = backtest(OPTIMIZE_CONFIG['stake_amount'], PROCESSED, stoploss=params['stoploss'])
results = backtest({'stake_amount': OPTIMIZE_CONFIG['stake_amount'],
'processed': PROCESSED,
'stoploss': params['stoploss']})
result_explanation = format_results(results)
total_profit = results.profit_percent.sum()
@ -201,53 +444,8 @@ def format_results(results: DataFrame):
)
def buy_strategy_generator(params):
def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
if params['uptrend_long_ema']['enabled']:
conditions.append(dataframe['ema50'] > dataframe['ema100'])
if params['uptrend_short_ema']['enabled']:
conditions.append(dataframe['ema5'] > dataframe['ema10'])
if params['mfi']['enabled']:
conditions.append(dataframe['mfi'] < params['mfi']['value'])
if params['fastd']['enabled']:
conditions.append(dataframe['fastd'] < params['fastd']['value'])
if params['adx']['enabled']:
conditions.append(dataframe['adx'] > params['adx']['value'])
if params['rsi']['enabled']:
conditions.append(dataframe['rsi'] < params['rsi']['value'])
if params['over_sar']['enabled']:
conditions.append(dataframe['close'] > dataframe['sar'])
if params['green_candle']['enabled']:
conditions.append(dataframe['close'] > dataframe['open'])
if params['uptrend_sma']['enabled']:
prevsma = dataframe['sma'].shift(1)
conditions.append(dataframe['sma'] > prevsma)
# TRIGGERS
triggers = {
'lower_bb': dataframe['tema'] <= dataframe['blower'],
'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
}
conditions.append(triggers.get(params['trigger']['type']))
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
return populate_buy_trend
def start(args):
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
global TOTAL_TRIES, PROCESSED, TRIALS, _CURRENT_TRIES
TOTAL_TRIES = args.epochs
@ -262,8 +460,18 @@ def start(args):
logger.info('Using config: %s ...', args.config)
config = load_config(args.config)
pairs = config['exchange']['pair_whitelist']
PROCESSED = optimize.preprocess(optimize.load_data(
args.datadir, pairs=pairs, ticker_interval=args.ticker_interval))
# init the strategy to use
config.update({'strategy': args.strategy})
strategy = Strategy()
strategy.init(config)
timerange = misc.parse_timerange(args.timerange)
data = optimize.load_data(args.datadir, pairs=pairs,
ticker_interval=args.ticker_interval,
timerange=timerange)
optimize.populate_indicators = populate_indicators
PROCESSED = optimize.tickerdata_to_dataframe(data)
if args.mongodb:
logger.info('Using mongodb ...')
@ -287,7 +495,7 @@ def start(args):
try:
best_parameters = fmin(
fn=optimizer,
space=SPACE,
space=hyperopt_space(),
algo=tpe.suggest,
max_evals=TOTAL_TRIES,
trials=TRIALS
@ -303,9 +511,14 @@ def start(args):
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(SPACE, best_parameters)
best_parameters = space_eval(
hyperopt_space(),
best_parameters
)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s', generate_roi_table(best_parameters))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time

15
freqtrade/persistence.py
View File

@ -47,6 +47,10 @@ def init(config: dict, engine: Optional[Engine] = None) -> None:
Trade.query = session.query_property()
_DECL_BASE.metadata.create_all(engine)
# Clean dry_run DB
if _CONF.get('dry_run', False) and _CONF.get('dry_run_db', False):
clean_dry_run_db()
def cleanup() -> None:
"""
@ -56,6 +60,17 @@ def cleanup() -> None:
Trade.session.flush()
def clean_dry_run_db() -> None:
"""
Remove open_order_id from a Dry_run DB
:return: None
"""
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
# Check we are updating only a dry_run order not a prod one
if 'dry_run' in trade.open_order_id:
trade.open_order_id = None
class Trade(_DECL_BASE):
__tablename__ = 'trades'

19
freqtrade/rpc/__init__.py
View File

@ -155,7 +155,6 @@ def rpc_daily_profit(timescale, stake_currency, fiat_display_currency):
if not (isinstance(timescale, int) and timescale > 0):
return (True, '*Daily [n]:* `must be an integer greater than 0`')
# FIX: we might not want to call CryptoToFiatConverter, for every call
fiat = CryptoToFiatConverter()
for day in range(0, timescale):
profitday = today - timedelta(days=day)
@ -166,20 +165,27 @@ def rpc_daily_profit(timescale, stake_currency, fiat_display_currency):
.order_by(Trade.close_date)\
.all()
curdayprofit = sum(trade.calc_profit() for trade in trades)
profit_days[profitday] = format(curdayprofit, '.8f')
profit_days[profitday] = {
'amount': format(curdayprofit, '.8f'),
'trades': len(trades)
}
stats = [
[
key,
'{value:.8f} {symbol}'.format(value=float(value), symbol=stake_currency),
'{value:.8f} {symbol}'.format(
value=float(value['amount']),
symbol=stake_currency
),
'{value:.3f} {symbol}'.format(
value=fiat.convert_amount(
value,
value['amount'],
stake_currency,
fiat_display_currency
),
symbol=fiat_display_currency
)
),
'{value} trade{s}'.format(value=value['trades'], s='' if value['trades'] < 2 else 's'),
]
for key, value in profit_days.items()
]
@ -296,6 +302,7 @@ def rpc_trade_statistics(stake_currency, fiat_display_currency) -> None:
)
return markdown_msg
def rpc_balance(fiat_display_currency):
"""
:return: current account balance per crypto
@ -326,4 +333,4 @@ def rpc_balance(fiat_display_currency):
fiat = CryptoToFiatConverter()
symbol = fiat_display_currency
value = fiat.convert_amount(total, 'BTC', symbol)
return (output, total, symbol, value)
return (False, (output, total, symbol, value))

14
freqtrade/rpc/telegram.py
View File

@ -136,8 +136,8 @@ def _status(bot: Bot, update: Update) -> None:
if error:
send_msg(trades, bot=bot)
else:
for message in trades:
send_msg(message, bot=bot)
for trademsg in trades:
send_msg(trademsg, bot=bot)
@authorized_only
@ -252,11 +252,11 @@ def _balance(bot: Bot, update: Update) -> None:
(currencys, total, symbol, value) = result
output = ''
for currency in currencys:
output += """*Currency*: {Currency}
*Available*: {Available}
*Balance*: {Balance}
*Pending*: {Pending}
*Est. BTC*: {BTC: .8f}
output += """*Currency*: {currency}
*Available*: {available}
*Balance*: {balance}
*Pending*: {pending}
*Est. BTC*: {est_btc: .8f}
""".format(**currency)

0
freqtrade/strategy/__init__.py Normal file
View File

238
freqtrade/strategy/default_strategy.py Normal file
View File

@ -0,0 +1,238 @@
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
class_name = 'DefaultStrategy'
class DefaultStrategy(IStrategy):
"""
Default Strategy provided by freqtrade bot.
You can override it with your own strategy
"""
# Minimal ROI designed for the strategy
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = 5
def populate_indicators(self, dataframe: DataFrame) -> 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.
"""
# 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:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['fastd'] < 35) &
(dataframe['adx'] > 30) &
(dataframe['plus_di'] > 0.5)
) |
(
(dataframe['adx'] > 65) &
(dataframe['plus_di'] > 0.5)
),
'buy'] = 1
return 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[
(
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
) &
(dataframe['adx'] > 10) &
(dataframe['minus_di'] > 0)
) |
(
(dataframe['adx'] > 70) &
(dataframe['minus_di'] > 0.5)
),
'sell'] = 1
return dataframe

44
freqtrade/strategy/interface.py Normal file
View File

@ -0,0 +1,44 @@
from abc import ABC, abstractmethod
from pandas import DataFrame
class IStrategy(ABC):
@property
def name(self) -> str:
"""
Name of the strategy.
:return: str representation of the class name
"""
return self.__class__.__name__
"""
Attributes you can use:
minimal_roi -> Dict: Minimal ROI designed for the strategy
stoploss -> float: optimal stoploss designed for the strategy
ticker_interval -> int: value of the ticker interval to use for the strategy
"""
@abstractmethod
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:return: a Dataframe with all mandatory indicators for the strategies
"""
@abstractmethod
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
:return:
"""
@abstractmethod
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
"""

166
freqtrade/strategy/strategy.py Normal file
View File

@ -0,0 +1,166 @@
import os
import sys
import logging
import importlib
from pandas import DataFrame
from typing import Dict
from freqtrade.strategy.interface import IStrategy
sys.path.insert(0, r'../../user_data/strategies')
class Strategy(object):
__instance = None
DEFAULT_STRATEGY = 'default_strategy'
def __new__(cls):
if Strategy.__instance is None:
Strategy.__instance = object.__new__(cls)
return Strategy.__instance
def init(self, config):
self.logger = logging.getLogger(__name__)
# Verify the strategy is in the configuration, otherwise fallback to the default strategy
if 'strategy' in config:
strategy = config['strategy']
else:
strategy = self.DEFAULT_STRATEGY
# Load the strategy
self._load_strategy(strategy)
# Set attributes
# Check if we need to override configuration
if 'minimal_roi' in config:
self.custom_strategy.minimal_roi = config['minimal_roi']
self.logger.info("Override strategy \'minimal_roi\' with value in config file.")
if 'stoploss' in config:
self.custom_strategy.stoploss = config['stoploss']
self.logger.info(
"Override strategy \'stoploss\' with value in config file: {}.".format(
config['stoploss']
)
)
if 'ticker_interval' in config:
self.custom_strategy.ticker_interval = config['ticker_interval']
self.logger.info(
"Override strategy \'ticker_interval\' with value in config file: {}.".format(
config['ticker_interval']
)
)
self.minimal_roi = self.custom_strategy.minimal_roi
self.stoploss = self.custom_strategy.stoploss
self.ticker_interval = self.custom_strategy.ticker_interval
def _load_strategy(self, strategy_name: str) -> None:
"""
Search and load the custom strategy. If no strategy found, fallback on the default strategy
Set the object into self.custom_strategy
:param strategy_name: name of the module to import
:return: None
"""
try:
# Start by sanitizing the file name (remove any extensions)
strategy_name = self._sanitize_module_name(filename=strategy_name)
# Search where can be the strategy file
path = self._search_strategy(filename=strategy_name)
# Load the strategy
self.custom_strategy = self._load_class(path + strategy_name)
# Fallback to the default strategy
except (ImportError, TypeError):
self.custom_strategy = self._load_class('.' + self.DEFAULT_STRATEGY)
def _load_class(self, filename: str) -> IStrategy:
"""
Import a strategy as a module
:param filename: path to the strategy (path from freqtrade/strategy/)
:return: return the strategy class
"""
module = importlib.import_module(filename, __package__)
custom_strategy = getattr(module, module.class_name)
self.logger.info("Load strategy class: {} ({}.py)".format(module.class_name, filename))
return custom_strategy()
@staticmethod
def _sanitize_module_name(filename: str) -> str:
"""
Remove any extension from filename
:param filename: filename to sanatize
:return: return the filename without extensions
"""
filename = os.path.basename(filename)
filename = os.path.splitext(filename)[0]
return filename
@staticmethod
def _search_strategy(filename: str) -> str:
"""
Search for the Strategy file in different folder
1. search into the user_data/strategies folder
2. search into the freqtrade/strategy folder
3. if nothing found, return None
:param strategy_name: module name to search
:return: module path where is the strategy
"""
pwd = os.path.dirname(os.path.realpath(__file__)) + '/'
user_data = os.path.join(pwd, '..', '..', 'user_data', 'strategies', filename + '.py')
strategy_folder = os.path.join(pwd, filename + '.py')
path = None
if os.path.isfile(user_data):
path = 'user_data.strategies.'
elif os.path.isfile(strategy_folder):
path = '.'
return path
def minimal_roi(self) -> Dict:
"""
Minimal ROI designed for the strategy
:return: Dict: Value for the Minimal ROI
"""
return
def stoploss(self) -> float:
"""
Optimal stoploss designed for the strategy
:return: float | return None to disable it
"""
return self.custom_strategy.stoploss
def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Populate indicators that will be used in the Buy and Sell strategy
:param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
:return: a Dataframe with all mandatory indicators for the strategies
"""
return self.custom_strategy.populate_indicators(dataframe)
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
:return:
"""
return self.custom_strategy.populate_buy_trend(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
"""
return self.custom_strategy.populate_sell_trend(dataframe)

40
freqtrade/tests/conftest.py
View File

@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring
from datetime import datetime
from unittest.mock import MagicMock
from functools import reduce
import arrow
import pytest
@ -10,6 +11,14 @@ from telegram import Chat, Message, Update
from freqtrade.misc import CONF_SCHEMA
def log_has(line, logs):
# caplog mocker returns log as a tuple: ('freqtrade.analyze', logging.WARNING, 'foobar')
# and we want to match line against foobar in the tuple
return reduce(lambda a, b: a or b,
filter(lambda x: x[2] == line, logs),
False)
@pytest.fixture(scope="module")
def default_conf():
""" Returns validated configuration suitable for most tests """
@ -18,6 +27,7 @@ def default_conf():
"stake_currency": "BTC",
"stake_amount": 0.001,
"fiat_display_currency": "USD",
"ticker_interval": 5,
"dry_run": True,
"minimal_roi": {
"40": 0.0,
@ -216,3 +226,33 @@ def ticker_history():
"BV": 0.7039405
}
]
@pytest.fixture
def ticker_history_without_bv():
return [
{
"O": 8.794e-05,
"H": 8.948e-05,
"L": 8.794e-05,
"C": 8.88e-05,
"V": 991.09056638,
"T": "2017-11-26T08:50:00"
},
{
"O": 8.88e-05,
"H": 8.942e-05,
"L": 8.88e-05,
"C": 8.893e-05,
"V": 658.77935965,
"T": "2017-11-26T08:55:00"
},
{
"O": 8.891e-05,
"H": 8.893e-05,
"L": 8.875e-05,
"C": 8.877e-05,
"V": 7920.73570705,
"T": "2017-11-26T09:00:00"
}
]

87
freqtrade/tests/exchange/test_exchange.py
View File

@ -7,13 +7,23 @@ import pytest
from freqtrade import OperationalException
from freqtrade.exchange import init, validate_pairs, buy, sell, get_balance, get_balances, \
get_ticker, cancel_order, get_name, get_fee
get_ticker, get_ticker_history, cancel_order, get_name, get_fee
import freqtrade.exchange as exchange
API_INIT = False
def maybe_init_api(conf, mocker):
global API_INIT
if not API_INIT:
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(config=conf)
API_INIT = True
def test_init(default_conf, mocker, caplog):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
init(config=default_conf)
maybe_init_api(default_conf, mocker)
assert ('freqtrade.exchange',
logging.INFO,
'Instance is running with dry_run enabled'
@ -159,8 +169,10 @@ def test_get_balances_prod(default_conf, mocker):
assert get_balances()[0]['Pending'] == 0.0
def test_get_ticker(mocker, ticker):
# This test is somewhat redundant with
# test_exchange_bittrex.py::test_exchange_bittrex_get_ticker
def test_get_ticker(default_conf, mocker, ticker):
maybe_init_api(default_conf, mocker)
api_mock = MagicMock()
tick = {"success": True, 'result': {'Bid': 0.00001098, 'Ask': 0.00001099, 'Last': 0.0001}}
api_mock.get_ticker = MagicMock(return_value=tick)
@ -177,6 +189,7 @@ def test_get_ticker(mocker, ticker):
mocker.patch('freqtrade.exchange.bittrex._API', api_mock)
# if not caching the result we should get the same ticker
# if not fetching a new result we should get the cached ticker
ticker = get_ticker(pair='BTC_ETH', refresh=False)
assert ticker['bid'] == 0.00001098
assert ticker['ask'] == 0.00001099
@ -187,6 +200,26 @@ def test_get_ticker(mocker, ticker):
assert ticker['ask'] == 1
def test_get_ticker_history(default_conf, mocker, ticker):
api_mock = MagicMock()
tick = 123
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# retrieve original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
# change the ticker
tick = 999
api_mock.get_ticker_history = MagicMock(return_value=tick)
mocker.patch('freqtrade.exchange._API', api_mock)
# ensure caching will still return the original ticker
ticks = get_ticker_history('BTC_ETH', int(default_conf['ticker_interval']))
assert ticks == 123
def test_cancel_order_dry_run(default_conf, mocker):
default_conf['dry_run'] = True
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
@ -194,6 +227,33 @@ def test_cancel_order_dry_run(default_conf, mocker):
assert cancel_order(order_id='123') is None
# Ensure that if not dry_run, we should call API
def test_cancel_order(default_conf, mocker):
default_conf['dry_run'] = False
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
api_mock = MagicMock()
api_mock.cancel_order = MagicMock(return_value=123)
mocker.patch('freqtrade.exchange._API', api_mock)
assert cancel_order(order_id='_') == 123
def test_get_order(default_conf, mocker):
default_conf['dry_run'] = True
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
order = MagicMock()
order.myid = 123
exchange._DRY_RUN_OPEN_ORDERS['X'] = order
print(exchange.get_order('X'))
assert exchange.get_order('X').myid == 123
default_conf['dry_run'] = False
mocker.patch.dict('freqtrade.exchange._CONF', default_conf)
api_mock = MagicMock()
api_mock.get_order = MagicMock(return_value=456)
mocker.patch('freqtrade.exchange._API', api_mock)
assert 456 == exchange.get_order('X')
def test_get_name(default_conf, mocker):
mocker.patch('freqtrade.exchange.validate_pairs',
side_effect=lambda s: True)
@ -209,3 +269,18 @@ def test_get_fee(default_conf, mocker):
init(default_conf)
assert get_fee() == 0.0025
def test_exchange_misc(default_conf, mocker):
api_mock = MagicMock()
mocker.patch('freqtrade.exchange._API', api_mock)
exchange.get_markets()
assert 1 == api_mock.get_markets.call_count
exchange.get_market_summaries()
assert 1 == api_mock.get_market_summaries.call_count
api_mock.name = 123
assert 123 == exchange.get_name()
api_mock.fee = 456
assert 456 == exchange.get_fee()
exchange.get_wallet_health()
assert 1 == api_mock.get_wallet_health.call_count

22
freqtrade/tests/exchange/test_exchange_bittrex.py
View File

@ -143,7 +143,7 @@ def test_exchange_bittrex_fee():
assert fee >= 0 and fee < 0.1 # Fee is 0-10 %
def test_exchange_bittrex_buy_good(mocker):
def test_exchange_bittrex_buy_good():
wb = make_wrap_bittrex()
fb = FakeBittrex()
uuid = wb.buy('BTC_ETH', 1, 1)
@ -154,7 +154,7 @@ def test_exchange_bittrex_buy_good(mocker):
wb.buy('BAD', 1, 1)
def test_exchange_bittrex_sell_good(mocker):
def test_exchange_bittrex_sell_good():
wb = make_wrap_bittrex()
fb = FakeBittrex()
uuid = wb.sell('BTC_ETH', 1, 1)
@ -165,7 +165,7 @@ def test_exchange_bittrex_sell_good(mocker):
uuid = wb.sell('BAD', 1, 1)
def test_exchange_bittrex_get_balance(mocker):
def test_exchange_bittrex_get_balance():
wb = make_wrap_bittrex()
fb = FakeBittrex()
bal = wb.get_balance('BTC_ETH')
@ -232,11 +232,18 @@ def test_exchange_bittrex_get_ticker_bad():
with pytest.raises(btx.OperationalException, match=r'.*gone bad.*'):
wb.get_ticker('BTC_ETH')
fb.result = {'success': True,
'result': {'Bid': 1, 'Ask': 0, 'Last': None}} # incomplete result
with pytest.raises(ContentDecodingError, match=r'.*Got invalid response from bittrex params.*'):
wb.get_ticker('BTC_ETH')
def test_exchange_bittrex_get_ticker_history_one():
def test_exchange_bittrex_get_ticker_history_intervals():
wb = make_wrap_bittrex()
FakeBittrex()
assert wb.get_ticker_history('BTC_ETH', 1)
for tick_interval in [1, 5, 30, 60, 1440]:
assert ([{'C': 0, 'V': 0, 'O': 0, 'H': 0, 'L': 0, 'T': 0}] ==
wb.get_ticker_history('BTC_ETH', tick_interval))
def test_exchange_bittrex_get_ticker_history():
@ -346,3 +353,8 @@ def test_validate_response_min_trade_requirement_not_met():
}
with pytest.raises(ContentDecodingError, match=r'.*MIN_TRADE_REQUIREMENT_NOT_MET.*'):
Bittrex._validate_response(response)
def test_custom_requests(mocker):
mocker.patch('freqtrade.exchange.bittrex.requests', MagicMock())
btx.custom_requests('http://', '')

73
freqtrade/tests/optimize/test_backtesting.py
View File

@ -3,12 +3,28 @@
import logging
import math
import pandas as pd
import pytest
from unittest.mock import MagicMock
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
from freqtrade.optimize import preprocess
from freqtrade.optimize.backtesting import backtest, generate_text_table, get_timeframe
import freqtrade.optimize.backtesting as backtesting
from freqtrade.strategy.strategy import Strategy
@pytest.fixture
def default_strategy():
strategy = Strategy()
strategy.init({'strategy': 'default_strategy'})
return strategy
def trim_dictlist(dl, num):
new = {}
for pair, pair_data in dl.items():
new[pair] = pair_data[num:]
return new
def test_generate_text_table():
@ -30,7 +46,7 @@ def test_generate_text_table():
'TOTAL 2 15.00 0.60000000 100.0 2 0') # noqa
def test_get_timeframe():
def test_get_timeframe(default_strategy):
data = preprocess(optimize.load_data(
None, ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data)
@ -38,37 +54,36 @@ def test_get_timeframe():
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def test_backtest(default_conf, mocker):
def test_backtest(default_strategy, default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
data = trim_dictlist(data, -200)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty
def test_backtest_1min_ticker_interval(default_conf, mocker):
def test_backtest_1min_ticker_interval(default_strategy, default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 1, True)
data = trim_dictlist(data, -200)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 1,
'realistic': True})
assert not results.empty
def trim_dictlist(dl, num):
new = {}
for pair, pair_data in dl.items():
new[pair] = pair_data[num:]
return new
def load_data_test(what):
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -100)
timerange = ((None, 'line'), None, -100)
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'], timerange=timerange)
pair = data['BTC_UNITEST']
datalen = len(pair)
# Depending on the what parameter we now adjust the
@ -113,7 +128,10 @@ def simple_backtest(config, contour, num_results):
data = load_data_test(contour)
processed = optimize.preprocess(data)
assert isinstance(processed, dict)
results = backtest(config['stake_amount'], processed, 1, True)
results = backtest({'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'realistic': True})
# results :: <class 'pandas.core.frame.DataFrame'>
assert len(results) == num_results
@ -122,15 +140,18 @@ def simple_backtest(config, contour, num_results):
# loaded by freqdata/optimize/__init__.py::load_data()
def test_backtest2(default_conf, mocker):
def test_backtest2(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
data = trim_dictlist(data, -200)
results = backtest({'stake_amount': default_conf['stake_amount'],
'processed': optimize.preprocess(data),
'max_open_trades': 10,
'realistic': True})
assert not results.empty
def test_processed(default_conf, mocker):
def test_processed(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
dict_of_tickerrows = load_data_test('raise')
dataframes = optimize.preprocess(dict_of_tickerrows)
@ -142,17 +163,17 @@ def test_processed(default_conf, mocker):
assert col in cols
def test_backtest_pricecontours(default_conf, mocker):
def test_backtest_pricecontours(default_conf, mocker, default_strategy):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
tests = [['raise', 17], ['lower', 0], ['sine', 17]]
for [contour, numres] in tests:
simple_backtest(default_conf, contour, numres)
def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1)
def mocked_load_data(datadir, pairs=[], ticker_interval=0, refresh_pairs=False, timerange=None):
tickerdata = optimize.load_tickerdata_file(datadir, 'BTC_UNITEST', 1, timerange=timerange)
pairdata = {'BTC_UNITEST': tickerdata}
return trim_dictlist(pairdata, -100)
return pairdata
def test_backtest_start(default_conf, mocker, caplog):
@ -166,6 +187,8 @@ def test_backtest_start(default_conf, mocker, caplog):
args.level = 10
args.live = False
args.datadir = None
args.export = None
args.timerange = '-100' # needed due to MagicMock malleability
backtesting.start(args)
# check the logs, that will contain the backtest result
exists = ['Using max_open_trades: 1 ...',

45
freqtrade/tests/optimize/test_hyperopt.py
View File

@ -1,6 +1,6 @@
# pragma pylint: disable=missing-docstring,W0212,C0103
from freqtrade.optimize.hyperopt import calculate_loss, TARGET_TRADES, EXPECTED_MAX_PROFIT, start, \
log_results, save_trials, read_trials
log_results, save_trials, read_trials, generate_roi_table
def test_loss_calculation_prefer_correct_trade_count():
@ -54,6 +54,7 @@ def create_trials(mocker):
def test_start_calls_fmin(mocker):
trials = create_trials(mocker)
mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
mocker.patch('freqtrade.optimize.hyperopt.TRIALS', return_value=trials)
mocker.patch('freqtrade.optimize.hyperopt.sorted',
return_value=trials.results)
@ -61,7 +62,8 @@ def test_start_calls_fmin(mocker):
mocker.patch('freqtrade.optimize.load_data')
mock_fmin = mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False)
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=False,
timerange=None)
start(args)
mock_fmin.assert_called_once()
@ -70,11 +72,12 @@ def test_start_calls_fmin(mocker):
def test_start_uses_mongotrials(mocker):
mock_mongotrials = mocker.patch('freqtrade.optimize.hyperopt.MongoTrials',
return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value={})
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True)
args = mocker.Mock(epochs=1, config='config.json.example', mongodb=True,
timerange=None)
start(args)
mock_mongotrials.assert_called_once()
@ -107,6 +110,7 @@ def test_no_log_if_loss_does_not_improve(mocker):
def test_fmin_best_results(mocker, caplog):
fmin_result = {
"macd_below_zero": 0,
"adx": 1,
"adx-value": 15.0,
"fastd": 1,
@ -121,14 +125,21 @@ def test_fmin_best_results(mocker, caplog):
"uptrend_short_ema": 0,
"uptrend_sma": 0,
"stoploss": -0.1,
"roi_t1": 1,
"roi_t2": 2,
"roi_t3": 3,
"roi_p1": 1,
"roi_p2": 2,
"roi_p3": 3,
}
mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin', return_value=fmin_result)
args = mocker.Mock(epochs=1, config='config.json.example')
args = mocker.Mock(epochs=1, config='config.json.example',
timerange=None)
start(args)
exists = [
@ -136,7 +147,7 @@ def test_fmin_best_results(mocker, caplog):
'"adx": {\n "enabled": true,\n "value": 15.0\n },',
'"green_candle": {\n "enabled": true\n },',
'"mfi": {\n "enabled": false\n },',
'"trigger": {\n "type": "ao_cross_zero"\n },',
'"trigger": {\n "type": "faststoch10"\n },',
'"stoploss": -0.1',
]
@ -146,11 +157,12 @@ def test_fmin_best_results(mocker, caplog):
def test_fmin_throw_value_error(mocker, caplog):
mocker.patch('freqtrade.optimize.hyperopt.MongoTrials', return_value=create_trials(mocker))
mocker.patch('freqtrade.optimize.preprocess')
mocker.patch('freqtrade.optimize.tickerdata_to_dataframe')
mocker.patch('freqtrade.optimize.load_data')
mocker.patch('freqtrade.optimize.hyperopt.fmin', side_effect=ValueError())
args = mocker.Mock(epochs=1, config='config.json.example')
args = mocker.Mock(epochs=1, config='config.json.example',
timerange=None)
start(args)
exists = [
@ -184,7 +196,8 @@ def test_resuming_previous_hyperopt_results_succeeds(mocker):
return_value={})
args = mocker.Mock(epochs=1,
config='config.json.example',
mongodb=False)
mongodb=False,
timerange=None)
start(args)
@ -221,3 +234,15 @@ def test_read_trials_returns_trials_file(mocker):
assert read_trials() == trials
mock_open.assert_called_once()
mock_load.assert_called_once()
def test_roi_table_generation():
params = {
'roi_t1': 5,
'roi_t2': 10,
'roi_t3': 15,
'roi_p1': 1,
'roi_p2': 2,
'roi_p3': 3,
}
assert generate_roi_table(params) == {'0': 6, '15': 3, '25': 1, '30': 0}

2
freqtrade/tests/optimize/test_hyperopt_config.py
View File

@ -1,6 +1,6 @@
# pragma pylint: disable=missing-docstring,W0212
from freqtrade.optimize.hyperopt_conf import hyperopt_optimize_conf
from user_data.hyperopt_conf import hyperopt_optimize_conf
def test_hyperopt_optimize_conf():

64
freqtrade/tests/optimize/test_optimize.py
View File

@ -2,6 +2,7 @@
import os
import logging
# from unittest.mock import MagicMock
from shutil import copyfile
from freqtrade import exchange, optimize
from freqtrade.exchange import Bittrex
@ -43,6 +44,23 @@ def _clean_test_file(file: str) -> None:
os.rename(file_swp, file)
def test_load_data_30min_ticker(default_conf, ticker_history, mocker, caplog):
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file = 'freqtrade/tests/testdata/BTC_UNITTEST-30.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['BTC_UNITTEST'], ticker_interval=30)
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
'Download the pair: "BTC_ETH", Interval: 30 min'
) not in caplog.record_tuples
_clean_test_file(file)
def test_load_data_5min_ticker(default_conf, ticker_history, mocker, caplog):
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
@ -51,7 +69,7 @@ def test_load_data_5min_ticker(default_conf, ticker_history, mocker, caplog):
file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['BTC_ETH'])
optimize.load_data(None, pairs=['BTC_ETH'], ticker_interval=5)
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
@ -113,17 +131,28 @@ def test_download_pairs(default_conf, ticker_history, mocker):
_backup_file(file2_1)
_backup_file(file2_5)
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI']) is True
assert os.path.isfile(file1_1) is False
assert os.path.isfile(file2_1) is False
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=1) is True
assert os.path.isfile(file1_1) is True
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_1) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
_clean_test_file(file2_1)
assert os.path.isfile(file1_5) is False
assert os.path.isfile(file2_5) is False
assert download_pairs(None, pairs=['BTC-MEME', 'BTC-CFI'], ticker_interval=5) is True
assert os.path.isfile(file1_5) is True
assert os.path.isfile(file2_5) is True
# clean files freshly downloaded
_clean_test_file(file1_5)
_clean_test_file(file2_5)
@ -139,7 +168,7 @@ def test_download_pairs_exception(default_conf, ticker_history, mocker, caplog):
_backup_file(file1_1)
_backup_file(file1_5)
download_pairs(None, pairs=['BTC-MEME'])
download_pairs(None, pairs=['BTC-MEME'], ticker_interval=1)
# clean files freshly downloaded
_clean_test_file(file1_1)
_clean_test_file(file1_5)
@ -170,7 +199,30 @@ def test_download_backtesting_testdata(default_conf, ticker_history, mocker):
_clean_test_file(file2)
def test_download_backtesting_testdata2(default_conf, mocker):
tick = [{'T': 'bar'}, {'T': 'foo'}]
mocker.patch('freqtrade.misc.file_dump_json', return_value=None)
mocker.patch('freqtrade.optimize.__init__.get_ticker_history', return_value=tick)
assert download_backtesting_testdata(None, pair="BTC-UNITEST", interval=1)
assert download_backtesting_testdata(None, pair="BTC-UNITEST", interval=3)
def test_load_tickerdata_file():
assert not load_tickerdata_file(None, 'BTC_UNITEST', 7)
tickerdata = load_tickerdata_file(None, 'BTC_UNITEST', 1)
assert _btc_unittest_length == len(tickerdata)
def test_init(default_conf, mocker):
conf = {'exchange': {'pair_whitelist': []}}
mocker.patch('freqtrade.optimize.hyperopt_optimize_conf', return_value=conf)
assert {} == optimize.load_data('', pairs=[], refresh_pairs=True,
ticker_interval=int(default_conf['ticker_interval']))
def test_tickerdata_to_dataframe():
timerange = ((None, 'line'), None, -100)
tick = load_tickerdata_file(None, 'BTC_UNITEST', 1, timerange=timerange)
tickerlist = {'BTC_UNITEST': tick}
data = optimize.tickerdata_to_dataframe(tickerlist)
assert 100 == len(data['BTC_UNITEST'])

41
freqtrade/tests/rpc/test_rpc.py
View File

@ -72,7 +72,7 @@ def test_send_msg_telegram_disabled(mocker):
def test_rpc_trade_status(default_conf, update, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -92,7 +92,7 @@ def test_rpc_trade_status(default_conf, update, ticker, mocker):
assert error
assert result.find('no active trade') >= 0
main.create_trade(0.001)
main.create_trade(0.001, 5)
(error, result) = rpc.rpc_trade_status()
assert not error
trade = result[0]
@ -101,7 +101,7 @@ def test_rpc_trade_status(default_conf, update, ticker, mocker):
def test_rpc_daily_profit(default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -118,7 +118,7 @@ def test_rpc_daily_profit(default_conf, update, ticker, limit_buy_order, limit_s
fiat_display_currency = default_conf['fiat_display_currency']
# Create some test data
main.create_trade(0.001)
main.create_trade(0.001, 5)
trade = Trade.query.first()
assert trade
@ -154,7 +154,7 @@ def test_rpc_daily_profit(default_conf, update, ticker, limit_buy_order, limit_s
def test_rpc_trade_statistics(
default_conf, update, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -176,7 +176,7 @@ def test_rpc_trade_statistics(
assert stats.find('no closed trade') >= 0
# Create some test data
main.create_trade(0.001)
main.create_trade(0.001, 5)
trade = Trade.query.first()
# Simulate fulfilled LIMIT_BUY order for trade
trade.update(limit_buy_order)
@ -203,3 +203,32 @@ def test_rpc_trade_statistics(
assert stats['avg_duration'] == '0:00:00'
assert stats['best_pair'] == 'BTC_ETH'
assert prec_satoshi(stats['best_rate'], 6.2)
def test_rpc_balance_handle(default_conf, update, mocker):
mock_balance = [{
'Currency': 'BTC',
'Balance': 10.0,
'Available': 12.0,
'Pending': 0.0,
'CryptoAddress': 'XXXX',
}, {
'Currency': 'ETH',
'Balance': 0.0,
'Available': 0.0,
'Pending': 0.0,
'CryptoAddress': 'XXXX',
}]
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.main.exchange',
get_balances=MagicMock(return_value=mock_balance))
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
res = rpc.rpc_balance(default_conf['fiat_display_currency'])
assert res
# FIX: check returned result
# res::
# (False, ([{'currency': 'BTC', 'available': 12.0, 'balance': 10.0,
# 'pending': 0.0, 'est_btc': 10.0}], 10.0, 'USD', 150000.0))

84
freqtrade/tests/rpc/test_rpc_telegram.py
View File

@ -77,7 +77,7 @@ def test_authorized_only_exception(default_conf, mocker):
def test_status_handle(default_conf, update, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
@ -102,7 +102,7 @@ def test_status_handle(default_conf, update, ticker, mocker):
msg_mock.reset_mock()
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
# Trigger status while we have a fulfilled order for the open trade
_status(bot=MagicMock(), update=update)
@ -112,7 +112,7 @@ def test_status_handle(default_conf, update, ticker, mocker):
def test_status_table_handle(default_conf, update, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple(
@ -138,7 +138,7 @@ def test_status_table_handle(default_conf, update, ticker, mocker):
msg_mock.reset_mock()
# Create some test data
create_trade(15.0)
create_trade(15.0, int(default_conf['ticker_interval']))
_status_table(bot=MagicMock(), update=update)
@ -154,7 +154,7 @@ def test_status_table_handle(default_conf, update, ticker, mocker):
def test_profit_handle(
default_conf, update, ticker, ticker_sell_up, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
@ -176,7 +176,7 @@ def test_profit_handle(
msg_mock.reset_mock()
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
# Simulate fulfilled LIMIT_BUY order for trade
@ -210,7 +210,7 @@ def test_profit_handle(
def test_forcesell_handle(default_conf, update, ticker, ticker_sell_up, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -219,13 +219,11 @@ def test_forcesell_handle(default_conf, update, ticker, ticker_sell_up, mocker):
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -239,7 +237,9 @@ def test_forcesell_handle(default_conf, update, ticker, ticker_sell_up, mocker):
_forcesell(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
@ -247,7 +247,7 @@ def test_forcesell_handle(default_conf, update, ticker, ticker_sell_up, mocker):
def test_forcesell_down_handle(default_conf, update, ticker, ticker_sell_down, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -256,13 +256,11 @@ def test_forcesell_down_handle(default_conf, update, ticker, ticker_sell_down, m
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
# Decrease the price and sell it
mocker.patch.multiple('freqtrade.main.exchange',
@ -276,7 +274,9 @@ def test_forcesell_down_handle(default_conf, update, ticker, ticker_sell_down, m
_forcesell(bot=MagicMock(), update=update)
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
@ -308,7 +308,7 @@ def test_exec_forcesell_open_orders(default_conf, ticker, mocker):
def test_forcesell_all_handle(default_conf, update, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -317,14 +317,12 @@ def test_forcesell_all_handle(default_conf, update, ticker, mocker):
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
init(default_conf, create_engine('sqlite://'))
# Create some test data
for _ in range(4):
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
rpc_mock.reset_mock()
update.message.text = '/forcesell all'
@ -339,7 +337,7 @@ def test_forcesell_all_handle(default_conf, update, ticker, mocker):
def test_forcesell_handle_invalid(default_conf, update, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
msg_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -376,7 +374,7 @@ def test_forcesell_handle_invalid(default_conf, update, mocker):
def test_performance_handle(
default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
@ -389,7 +387,7 @@ def test_performance_handle(
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -410,7 +408,7 @@ def test_performance_handle(
def test_daily_handle(
default_conf, update, ticker, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
@ -427,7 +425,7 @@ def test_daily_handle(
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -448,6 +446,28 @@ def test_daily_handle(
assert str(datetime.utcnow().date()) in msg_mock.call_args_list[0][0][0]
assert str(' 0.00006217 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 0.933 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 1 trade') in msg_mock.call_args_list[0][0][0]
assert str(' 0 trade') in msg_mock.call_args_list[0][0][0]
# Reset msg_mock
msg_mock.reset_mock()
# Add two other trades
create_trade(0.001, int(default_conf['ticker_interval']))
create_trade(0.001, int(default_conf['ticker_interval']))
trades = Trade.query.all()
for trade in trades:
trade.update(limit_buy_order)
trade.update(limit_sell_order)
trade.close_date = datetime.utcnow()
trade.is_open = False
update.message.text = '/daily 1'
_daily(bot=MagicMock(), update=update)
assert str(' 0.00018651 BTC') in msg_mock.call_args_list[0][0][0]
assert str(' 2.798 USD') in msg_mock.call_args_list[0][0][0]
assert str(' 3 trades') in msg_mock.call_args_list[0][0][0]
# Try invalid data
msg_mock.reset_mock()
@ -460,7 +480,7 @@ def test_daily_handle(
def test_count_handle(default_conf, update, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
msg_mock = MagicMock()
mocker.patch.multiple(
'freqtrade.rpc.telegram',
@ -480,7 +500,7 @@ def test_count_handle(default_conf, update, ticker, mocker):
update_state(State.RUNNING)
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
msg_mock.reset_mock()
_count(bot=MagicMock(), update=update)
@ -492,7 +512,7 @@ def test_count_handle(default_conf, update, ticker, mocker):
def test_performance_handle_invalid(default_conf, update, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
msg_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc.telegram',
_CONF=default_conf,
@ -584,7 +604,7 @@ def test_stop_handle_already_stopped(default_conf, update, mocker):
assert 'already stopped' in msg_mock.call_args_list[0][0][0]
def test_balance_handle(default_conf, update, mocker):
def test_telegram_balance_handle(default_conf, update, mocker):
mock_balance = [{
'Currency': 'BTC',
'Balance': 10.0,

36
freqtrade/tests/strategy/test_default_strategy.py Normal file
View File

@ -0,0 +1,36 @@
import json
import pytest
from pandas import DataFrame
from freqtrade.strategy.default_strategy import DefaultStrategy, class_name
from freqtrade.analyze import parse_ticker_dataframe
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
return parse_ticker_dataframe(json.load(data_file))
def test_default_strategy_class_name():
assert class_name == DefaultStrategy.__name__
def test_default_strategy_structure():
assert hasattr(DefaultStrategy, 'minimal_roi')
assert hasattr(DefaultStrategy, 'stoploss')
assert hasattr(DefaultStrategy, 'ticker_interval')
assert hasattr(DefaultStrategy, 'populate_indicators')
assert hasattr(DefaultStrategy, 'populate_buy_trend')
assert hasattr(DefaultStrategy, 'populate_sell_trend')
def test_default_strategy(result):
strategy = DefaultStrategy()
assert type(strategy.minimal_roi) is dict
assert type(strategy.stoploss) is float
assert type(strategy.ticker_interval) is int
indicators = strategy.populate_indicators(result)
assert type(indicators) is DataFrame
assert type(strategy.populate_buy_trend(indicators)) is DataFrame
assert type(strategy.populate_sell_trend(indicators)) is DataFrame

141
freqtrade/tests/strategy/test_strategy.py Normal file
View File

@ -0,0 +1,141 @@
import json
import logging
import pytest
from freqtrade.strategy.strategy import Strategy
from freqtrade.analyze import parse_ticker_dataframe
@pytest.fixture
def result():
with open('freqtrade/tests/testdata/BTC_ETH-1.json') as data_file:
return parse_ticker_dataframe(json.load(data_file))
def test_sanitize_module_name():
assert Strategy._sanitize_module_name('default_strategy') == 'default_strategy'
assert Strategy._sanitize_module_name('default_strategy.py') == 'default_strategy'
assert Strategy._sanitize_module_name('../default_strategy.py') == 'default_strategy'
assert Strategy._sanitize_module_name('../default_strategy') == 'default_strategy'
assert Strategy._sanitize_module_name('.default_strategy') == '.default_strategy'
assert Strategy._sanitize_module_name('foo-bar') == 'foo-bar'
assert Strategy._sanitize_module_name('foo/bar') == 'bar'
def test_search_strategy():
assert Strategy._search_strategy('default_strategy') == '.'
assert Strategy._search_strategy('super_duper') is None
def test_strategy_structure():
assert hasattr(Strategy, 'init')
assert hasattr(Strategy, 'minimal_roi')
assert hasattr(Strategy, 'stoploss')
assert hasattr(Strategy, 'populate_indicators')
assert hasattr(Strategy, 'populate_buy_trend')
assert hasattr(Strategy, 'populate_sell_trend')
def test_load_strategy(result):
strategy = Strategy()
strategy.logger = logging.getLogger(__name__)
assert not hasattr(Strategy, 'custom_strategy')
strategy._load_strategy('default_strategy')
assert not hasattr(Strategy, 'custom_strategy')
assert hasattr(strategy.custom_strategy, 'populate_indicators')
assert 'adx' in strategy.populate_indicators(result)
def test_strategy(result):
strategy = Strategy()
strategy.init({'strategy': 'default_strategy'})
assert hasattr(strategy.custom_strategy, 'minimal_roi')
assert strategy.minimal_roi['0'] == 0.04
assert hasattr(strategy.custom_strategy, 'stoploss')
assert strategy.stoploss == -0.10
assert hasattr(strategy.custom_strategy, 'populate_indicators')
assert 'adx' in strategy.populate_indicators(result)
assert hasattr(strategy.custom_strategy, 'populate_buy_trend')
dataframe = strategy.populate_buy_trend(strategy.populate_indicators(result))
assert 'buy' in dataframe.columns
assert hasattr(strategy.custom_strategy, 'populate_sell_trend')
dataframe = strategy.populate_sell_trend(strategy.populate_indicators(result))
assert 'sell' in dataframe.columns
def test_strategy_override_minimal_roi(caplog):
config = {
'strategy': 'default_strategy',
'minimal_roi': {
"0": 0.5
}
}
strategy = Strategy()
strategy.init(config)
assert hasattr(strategy.custom_strategy, 'minimal_roi')
assert strategy.minimal_roi['0'] == 0.5
assert ('freqtrade.strategy.strategy',
logging.INFO,
'Override strategy \'minimal_roi\' with value in config file.'
) in caplog.record_tuples
def test_strategy_override_stoploss(caplog):
config = {
'strategy': 'default_strategy',
'stoploss': -0.5
}
strategy = Strategy()
strategy.init(config)
assert hasattr(strategy.custom_strategy, 'stoploss')
assert strategy.stoploss == -0.5
assert ('freqtrade.strategy.strategy',
logging.INFO,
'Override strategy \'stoploss\' with value in config file: -0.5.'
) in caplog.record_tuples
def test_strategy_override_ticker_interval(caplog):
config = {
'strategy': 'default_strategy',
'ticker_interval': 60
}
strategy = Strategy()
strategy.init(config)
assert hasattr(strategy.custom_strategy, 'ticker_interval')
assert strategy.ticker_interval == 60
assert ('freqtrade.strategy.strategy',
logging.INFO,
'Override strategy \'ticker_interval\' with value in config file: 60.'
) in caplog.record_tuples
def test_strategy_fallback_default_strategy():
strategy = Strategy()
strategy.logger = logging.getLogger(__name__)
assert not hasattr(Strategy, 'custom_strategy')
strategy._load_strategy('../../super_duper')
assert not hasattr(Strategy, 'custom_strategy')
def test_strategy_singleton():
strategy1 = Strategy()
strategy1.init({'strategy': 'default_strategy'})
assert hasattr(strategy1.custom_strategy, 'minimal_roi')
assert strategy1.minimal_roi['0'] == 0.04
strategy2 = Strategy()
assert hasattr(strategy2.custom_strategy, 'minimal_roi')
assert strategy2.minimal_roi['0'] == 0.04

77
freqtrade/tests/test_analyze.py
View File

@ -1,14 +1,17 @@
# pragma pylint: disable=missing-docstring,W0621
import json
from unittest.mock import MagicMock
import freqtrade.tests.conftest as tt # test tools
import arrow
import datetime
import pytest
from pandas import DataFrame
from freqtrade.analyze import (SignalType, get_signal, parse_ticker_dataframe,
from freqtrade.analyze import (get_signal, parse_ticker_dataframe,
populate_buy_trend, populate_indicators,
populate_sell_trend)
from freqtrade.strategy.strategy import Strategy
@pytest.fixture
@ -27,11 +30,17 @@ def test_dataframe_correct_length(result):
def test_populates_buy_trend(result):
# Load the default strategy for the unit test, because this logic is done in main.py
Strategy().init({'strategy': 'default_strategy'})
dataframe = populate_buy_trend(populate_indicators(result))
assert 'buy' in dataframe.columns
def test_populates_sell_trend(result):
# Load the default strategy for the unit test, because this logic is done in main.py
Strategy().init({'strategy': 'default_strategy'})
dataframe = populate_sell_trend(populate_indicators(result))
assert 'sell' in dataframe.columns
@ -40,30 +49,65 @@ def test_returns_latest_buy_signal(mocker):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'buy': 1, 'date': arrow.utcnow()}])
return_value=DataFrame([{'buy': 1, 'sell': 0, 'date': arrow.utcnow()}])
)
assert get_signal('BTC-ETH', SignalType.BUY)
assert get_signal('BTC-ETH', 5) == (True, False)
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'buy': 0, 'date': arrow.utcnow()}])
return_value=DataFrame([{'buy': 0, 'sell': 1, 'date': arrow.utcnow()}])
)
assert not get_signal('BTC-ETH', SignalType.BUY)
assert get_signal('BTC-ETH', 5) == (False, True)
def test_returns_latest_sell_signal(mocker):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=MagicMock())
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'sell': 1, 'date': arrow.utcnow()}])
return_value=DataFrame([{'sell': 1, 'buy': 0, 'date': arrow.utcnow()}])
)
assert get_signal('BTC-ETH', SignalType.SELL)
assert get_signal('BTC-ETH', 5) == (False, True)
mocker.patch(
'freqtrade.analyze.analyze_ticker',
return_value=DataFrame([{'sell': 0, 'date': arrow.utcnow()}])
return_value=DataFrame([{'sell': 0, 'buy': 1, 'date': arrow.utcnow()}])
)
assert not get_signal('BTC-ETH', SignalType.SELL)
assert get_signal('BTC-ETH', 5) == (True, False)
def test_get_signal_empty(default_conf, mocker, caplog):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=None)
assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
assert tt.log_has('Empty ticker history for pair foo',
caplog.record_tuples)
def test_get_signal_exception_valueerror(default_conf, mocker, caplog):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
mocker.patch('freqtrade.analyze.analyze_ticker',
side_effect=ValueError('xyz'))
assert (False, False) == get_signal('foo', int(default_conf['ticker_interval']))
assert tt.log_has('Unable to analyze ticker for pair foo: xyz',
caplog.record_tuples)
def test_get_signal_empty_dataframe(default_conf, mocker, caplog):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame([]))
assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
assert tt.log_has('Empty dataframe for pair xyz',
caplog.record_tuples)
def test_get_signal_old_dataframe(default_conf, mocker, caplog):
mocker.patch('freqtrade.analyze.get_ticker_history', return_value=1)
# FIX: The get_signal function has hardcoded 10, which we must inturn hardcode
oldtime = arrow.utcnow() - datetime.timedelta(minutes=11)
ticks = DataFrame([{'buy': 1, 'date': oldtime}])
mocker.patch('freqtrade.analyze.analyze_ticker', return_value=DataFrame(ticks))
assert (False, False) == get_signal('xyz', int(default_conf['ticker_interval']))
assert tt.log_has('Too old dataframe for pair xyz',
caplog.record_tuples)
def test_get_signal_handles_exceptions(mocker):
@ -71,4 +115,17 @@ def test_get_signal_handles_exceptions(mocker):
mocker.patch('freqtrade.analyze.analyze_ticker',
side_effect=Exception('invalid ticker history '))
assert not get_signal('BTC-ETH', SignalType.BUY)
assert get_signal('BTC-ETH', 5) == (False, False)
def test_parse_ticker_dataframe(ticker_history, ticker_history_without_bv):
columns = ['close', 'high', 'low', 'open', 'date', 'volume']
# Test file with BV data
dataframe = parse_ticker_dataframe(ticker_history)
assert dataframe.columns.tolist() == columns
# Test file without BV data
dataframe = parse_ticker_dataframe(ticker_history_without_bv)
assert dataframe.columns.tolist() == columns

6
freqtrade/tests/test_fiat_convert.py
View File

@ -116,9 +116,9 @@ def test_fiat_convert_get_price(mocker):
assert fiat_convert._pairs[0]._expiration is not expiration
def test_fiat_convert_without_network(mocker):
pymarketcap = MagicMock(side_effect=ImportError('Oh boy, you have no network!'))
mocker.patch('freqtrade.fiat_convert.Pymarketcap', pymarketcap)
def test_fiat_convert_without_network():
# Because CryptoToFiatConverter is a Singleton we reset the value of _coinmarketcap
CryptoToFiatConverter._coinmarketcap = None
fiat_convert = CryptoToFiatConverter()
assert fiat_convert._coinmarketcap is None

309
freqtrade/tests/test_main.py
View File

@ -2,6 +2,7 @@
import copy
import logging
from unittest.mock import MagicMock
import freqtrade.tests.conftest as tt # test tools
import arrow
import pytest
@ -10,7 +11,6 @@ from sqlalchemy import create_engine
import freqtrade.main as main
from freqtrade import DependencyException, OperationalException
from freqtrade.analyze import SignalType
from freqtrade.exchange import Exchanges
from freqtrade.main import (_process, check_handle_timedout, create_trade,
execute_sell, get_target_bid, handle_trade, init)
@ -21,11 +21,10 @@ from freqtrade.persistence import Trade
def test_parse_args_backtesting(mocker):
""" Test that main() can start backtesting or hyperopt.
and also ensure we can pass some specific arguments
argument parsing is done in test_misc.py """
further argument parsing is done in test_misc.py """
backtesting_mock = mocker.patch(
'freqtrade.optimize.backtesting.start', MagicMock())
with pytest.raises(SystemExit, match=r'0'):
main.main(['backtesting'])
main.main(['backtesting'])
assert backtesting_mock.call_count == 1
call_args = backtesting_mock.call_args[0][0]
assert call_args.config == 'config.json'
@ -39,8 +38,7 @@ def test_parse_args_backtesting(mocker):
def test_main_start_hyperopt(mocker):
hyperopt_mock = mocker.patch(
'freqtrade.optimize.hyperopt.start', MagicMock())
with pytest.raises(SystemExit, match=r'0'):
main.main(['hyperopt'])
main.main(['hyperopt'])
assert hyperopt_mock.call_count == 1
call_args = hyperopt_mock.call_args[0][0]
assert call_args.config == 'config.json'
@ -49,10 +47,38 @@ def test_main_start_hyperopt(mocker):
assert call_args.func is not None
def test_process_maybe_execute_buy(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.create_trade', return_value=True)
assert main.process_maybe_execute_buy(default_conf, int(default_conf['ticker_interval']))
mocker.patch('freqtrade.main.create_trade', return_value=False)
assert not main.process_maybe_execute_buy(default_conf, int(default_conf['ticker_interval']))
def test_process_maybe_execute_sell(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.handle_trade', return_value=True)
mocker.patch('freqtrade.exchange.get_order', return_value=1)
trade = MagicMock()
trade.open_order_id = '123'
assert not main.process_maybe_execute_sell(trade, int(default_conf['ticker_interval']))
trade.is_open = True
trade.open_order_id = None
# Assert we call handle_trade() if trade is feasible for execution
assert main.process_maybe_execute_sell(trade, int(default_conf['ticker_interval']))
def test_process_maybe_execute_buy_exception(default_conf, mocker, caplog):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.create_trade', MagicMock(side_effect=DependencyException))
main.process_maybe_execute_buy(default_conf, int(default_conf['ticker_interval']))
tt.log_has('Unable to create trade:', caplog.record_tuples)
def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -64,7 +90,7 @@ def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, m
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert not trades
result = _process()
result = _process(interval=int(default_conf['ticker_interval']))
assert result is True
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
@ -82,7 +108,7 @@ def test_process_trade_creation(default_conf, ticker, limit_buy_order, health, m
def test_process_exchange_failures(default_conf, ticker, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
sleep_mock = mocker.patch('time.sleep', side_effect=lambda _: None)
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -90,7 +116,7 @@ def test_process_exchange_failures(default_conf, ticker, health, mocker):
get_wallet_health=health,
buy=MagicMock(side_effect=requests.exceptions.RequestException))
init(default_conf, create_engine('sqlite://'))
result = _process()
result = _process(interval=int(default_conf['ticker_interval']))
assert result is False
assert sleep_mock.has_calls()
@ -99,7 +125,7 @@ def test_process_operational_exception(default_conf, ticker, health, mocker):
msg_mock = MagicMock()
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=msg_mock)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -108,7 +134,7 @@ def test_process_operational_exception(default_conf, ticker, health, mocker):
init(default_conf, create_engine('sqlite://'))
assert get_state() == State.RUNNING
result = _process()
result = _process(interval=int(default_conf['ticker_interval']))
assert result is False
assert get_state() == State.STOPPED
assert 'OperationalException' in msg_mock.call_args_list[-1][0][0]
@ -117,8 +143,7 @@ def test_process_operational_exception(default_conf, ticker, health, mocker):
def test_process_trade_handling(default_conf, ticker, limit_buy_order, health, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal',
side_effect=lambda *args: False if args[1] == SignalType.SELL else True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -129,18 +154,18 @@ def test_process_trade_handling(default_conf, ticker, limit_buy_order, health, m
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert not trades
result = _process()
result = _process(interval=int(default_conf['ticker_interval']))
assert result is True
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
assert len(trades) == 1
result = _process()
result = _process(interval=int(default_conf['ticker_interval']))
assert result is False
def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -150,7 +175,7 @@ def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
whitelist = copy.deepcopy(default_conf['exchange']['pair_whitelist'])
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade is not None
@ -171,7 +196,7 @@ def test_create_trade(default_conf, ticker, limit_buy_order, mocker):
def test_create_trade_minimal_amount(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
buy_mock = mocker.patch(
'freqtrade.main.exchange.buy', MagicMock(return_value='mocked_limit_buy')
)
@ -180,14 +205,14 @@ def test_create_trade_minimal_amount(default_conf, ticker, mocker):
get_ticker=ticker)
init(default_conf, create_engine('sqlite://'))
min_stake_amount = 0.0005
create_trade(min_stake_amount)
create_trade(min_stake_amount, int(default_conf['ticker_interval']))
rate, amount = buy_mock.call_args[0][1], buy_mock.call_args[0][2]
assert rate * amount >= min_stake_amount
def test_create_trade_no_stake_amount(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -195,12 +220,12 @@ def test_create_trade_no_stake_amount(default_conf, ticker, mocker):
buy=MagicMock(return_value='mocked_limit_buy'),
get_balance=MagicMock(return_value=default_conf['stake_amount'] * 0.5))
with pytest.raises(DependencyException, match=r'.*stake amount.*'):
create_trade(default_conf['stake_amount'])
create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
def test_create_trade_no_pairs(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -211,12 +236,12 @@ def test_create_trade_no_pairs(default_conf, ticker, mocker):
conf = copy.deepcopy(default_conf)
conf['exchange']['pair_whitelist'] = []
mocker.patch.dict('freqtrade.main._CONF', conf)
create_trade(default_conf['stake_amount'])
create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
def test_create_trade_no_pairs_after_blacklist(default_conf, ticker, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -228,12 +253,26 @@ def test_create_trade_no_pairs_after_blacklist(default_conf, ticker, mocker):
conf['exchange']['pair_whitelist'] = ["BTC_ETH"]
conf['exchange']['pair_blacklist'] = ["BTC_ETH"]
mocker.patch.dict('freqtrade.main._CONF', conf)
create_trade(default_conf['stake_amount'])
create_trade(default_conf['stake_amount'], int(default_conf['ticker_interval']))
def test_create_trade_no_signal(default_conf, ticker, mocker):
default_conf['dry_run'] = True
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', MagicMock(return_value=(False, False)))
mocker.patch.multiple('freqtrade.exchange',
get_ticker_history=MagicMock(return_value=20))
mocker.patch.multiple('freqtrade.main.exchange',
get_balance=MagicMock(return_value=20))
stake_amount = 10
Trade.query = MagicMock()
Trade.query.filter = MagicMock()
assert not create_trade(stake_amount, int(default_conf['ticker_interval']))
def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -248,7 +287,7 @@ def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -256,7 +295,8 @@ def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
trade.update(limit_buy_order)
assert trade.is_open is True
handle_trade(trade)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
assert trade.open_order_id == 'mocked_limit_sell'
# Simulate fulfilled LIMIT_SELL order for trade
@ -268,11 +308,57 @@ def test_handle_trade(default_conf, limit_buy_order, limit_sell_order, mocker):
assert trade.close_date is not None
def test_handle_overlpapping_signals(default_conf, ticker, mocker, caplog):
default_conf.update({'experimental': {'use_sell_signal': True}})
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
buy=MagicMock(return_value='mocked_limit_buy'))
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
init(default_conf, create_engine('sqlite://'))
create_trade(0.001, int(default_conf['ticker_interval']))
# Buy and Sell triggering, so doing nothing ...
trades = Trade.query.all()
assert len(trades) == 0
# Buy is triggering, so buying ...
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
create_trade(0.001, int(default_conf['ticker_interval']))
trades = Trade.query.all()
assert len(trades) == 1
assert trades[0].is_open is True
# Buy and Sell are not triggering, so doing nothing ...
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, False))
assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is False
trades = Trade.query.all()
assert len(trades) == 1
assert trades[0].is_open is True
# Buy and Sell are triggering, so doing nothing ...
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, True))
assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is False
trades = Trade.query.all()
assert len(trades) == 1
assert trades[0].is_open is True
# Sell is triggering, guess what : we are Selling!
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
trades = Trade.query.all()
assert handle_trade(trades[0], int(default_conf['ticker_interval'])) is True
def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
default_conf.update({'experimental': {'use_sell_signal': True}})
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -281,7 +367,7 @@ def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
mocker.patch('freqtrade.main.min_roi_reached', return_value=True)
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.is_open = True
@ -291,12 +377,12 @@ def test_handle_trade_roi(default_conf, ticker, mocker, caplog):
# we might just want to check if we are in a sell condition without
# executing
# if ROI is reached we must sell
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
assert handle_trade(trade)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, interval=int(default_conf['ticker_interval']))
assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
# if ROI is reached we must sell even if sell-signal is not signalled
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
assert handle_trade(trade)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, interval=int(default_conf['ticker_interval']))
assert ('freqtrade', logging.DEBUG, 'Executing sell due to ROI ...') in caplog.record_tuples
@ -304,7 +390,7 @@ def test_handle_trade_experimental(default_conf, ticker, mocker, caplog):
default_conf.update({'experimental': {'use_sell_signal': True}})
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -313,24 +399,23 @@ def test_handle_trade_experimental(default_conf, ticker, mocker, caplog):
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.is_open = True
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: False)
value_returned = handle_trade(trade)
assert ('freqtrade', logging.DEBUG, 'Checking sell_signal ...') in caplog.record_tuples
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, False))
value_returned = handle_trade(trade, int(default_conf['ticker_interval']))
assert value_returned is False
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
assert handle_trade(trade)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval']))
s = 'Executing sell due to sell signal ...'
assert ('freqtrade', logging.DEBUG, s) in caplog.record_tuples
def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -339,7 +424,7 @@ def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mo
# Create trade and sell it
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -349,13 +434,14 @@ def test_close_trade(default_conf, ticker, limit_buy_order, limit_sell_order, mo
assert trade.is_open is False
with pytest.raises(ValueError, match=r'.*closed trade.*'):
handle_trade(trade)
handle_trade(trade, int(default_conf['ticker_interval']))
def test_check_handle_timedout_buy(default_conf, ticker, limit_buy_order_old, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -380,14 +466,30 @@ def test_check_handle_timedout_buy(default_conf, ticker, limit_buy_order_old, mo
# check it does cancel buy orders over the time limit
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
assert rpc_mock.call_count == 1
trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
assert len(trades) == 0
def test_handle_timedout_limit_buy(default_conf, mocker):
cancel_order = MagicMock()
mocker.patch('freqtrade.exchange.cancel_order', cancel_order)
Trade.session = MagicMock()
trade = MagicMock()
order = {'remaining': 1,
'amount': 1}
assert main.handle_timedout_limit_buy(trade, order)
assert cancel_order.call_count == 1
order['amount'] = 2
assert not main.handle_timedout_limit_buy(trade, order)
assert cancel_order.call_count == 2
def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -413,14 +515,30 @@ def test_check_handle_timedout_sell(default_conf, ticker, limit_sell_order_old,
# check it does cancel sell orders over the time limit
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
assert rpc_mock.call_count == 1
assert trade_sell.is_open is True
def test_handle_timedout_limit_sell(default_conf, mocker):
cancel_order = MagicMock()
mocker.patch('freqtrade.exchange.cancel_order', cancel_order)
trade = MagicMock()
order = {'remaining': 1,
'amount': 1}
assert main.handle_timedout_limit_sell(trade, order)
assert cancel_order.call_count == 1
order['amount'] = 2
assert not main.handle_timedout_limit_sell(trade, order)
# Assert cancel_order was not called (callcount remains unchanged)
assert cancel_order.call_count == 1
def test_check_handle_timedout_partial(default_conf, ticker, limit_buy_order_old_partial,
mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
cancel_order_mock = MagicMock()
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker,
@ -446,6 +564,7 @@ def test_check_handle_timedout_partial(default_conf, ticker, limit_buy_order_old
# note this is for a partially-complete buy order
check_handle_timedout(600)
assert cancel_order_mock.call_count == 1
assert rpc_mock.call_count == 1
trades = Trade.query.filter(Trade.open_order_id.is_(trade_buy.open_order_id)).all()
assert len(trades) == 1
assert trades[0].amount == 23.0
@ -469,19 +588,17 @@ def test_balance_bigger_last_ask(mocker):
def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -494,7 +611,10 @@ def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert 'Profit' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert 'profit: 6.11%, 0.00006126' in rpc_mock.call_args_list[-1][0][0]
assert '0.919 USD' in rpc_mock.call_args_list[-1][0][0]
@ -502,7 +622,7 @@ def test_execute_sell_up(default_conf, ticker, ticker_sell_up, mocker):
def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.rpc.telegram',
@ -512,13 +632,11 @@ def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
mocker.patch.multiple('freqtrade.fiat_convert.Pymarketcap',
ticker=MagicMock(return_value={'price_usd': 15000.0}),
_cache_symbols=MagicMock(return_value={'BTC': 1}))
mocker.patch('freqtrade.fiat_convert.CryptoToFiatConverter._find_price', return_value=15000.0)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -531,15 +649,17 @@ def test_execute_sell_down(default_conf, ticker, ticker_sell_down, mocker):
execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
assert '-0.824 USD' in rpc_mock.call_args_list[-1][0][0]
def test_execute_sell_without_conf(default_conf, ticker, ticker_sell_up, mocker):
def test_execute_sell_without_conf_sell_down(default_conf, ticker, ticker_sell_down, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
@ -548,7 +668,38 @@ def test_execute_sell_without_conf(default_conf, ticker, ticker_sell_up, mocker)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
# Decrease the price and sell it
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker_sell_down)
mocker.patch('freqtrade.main._CONF', {})
execute_sell(trade=trade, limit=ticker_sell_down()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001044' in rpc_mock.call_args_list[-1][0][0]
assert 'loss: -5.48%, -0.00005492' in rpc_mock.call_args_list[-1][0][0]
def test_execute_sell_without_conf_sell_up(default_conf, ticker, ticker_sell_up, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch('freqtrade.rpc.init', MagicMock())
rpc_mock = mocker.patch('freqtrade.main.rpc.send_msg', MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
get_ticker=ticker)
init(default_conf, create_engine('sqlite://'))
# Create some test data
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
assert trade
@ -562,7 +713,9 @@ def test_execute_sell_without_conf(default_conf, ticker, ticker_sell_up, mocker)
execute_sell(trade=trade, limit=ticker_sell_up()['bid'])
assert rpc_mock.call_count == 2
assert 'Selling [BTC/ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Selling' in rpc_mock.call_args_list[-1][0][0]
assert '[BTC_ETH]' in rpc_mock.call_args_list[-1][0][0]
assert 'Amount' in rpc_mock.call_args_list[-1][0][0]
assert '0.00001172' in rpc_mock.call_args_list[-1][0][0]
assert '(profit: 6.11%, 0.00006126)' in rpc_mock.call_args_list[-1][0][0]
assert 'USD' not in rpc_mock.call_args_list[-1][0][0]
@ -576,7 +729,7 @@ def test_sell_profit_only_enable_profit(default_conf, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -588,11 +741,12 @@ def test_sell_profit_only_enable_profit(default_conf, limit_buy_order, mocker):
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
@ -603,7 +757,7 @@ def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -615,11 +769,12 @@ def test_sell_profit_only_disable_profit(default_conf, limit_buy_order, mocker):
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval'])) is True
def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
@ -630,7 +785,7 @@ def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -642,11 +797,12 @@ def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, mocker):
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is False
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval'])) is False
def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
@ -657,7 +813,7 @@ def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
mocker.patch('freqtrade.main.min_roi_reached', return_value=False)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: True)
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (True, False))
mocker.patch.multiple('freqtrade.rpc', init=MagicMock(), send_msg=MagicMock())
mocker.patch.multiple('freqtrade.main.exchange',
validate_pairs=MagicMock(),
@ -669,8 +825,9 @@ def test_sell_profit_only_disable_loss(default_conf, limit_buy_order, mocker):
buy=MagicMock(return_value='mocked_limit_buy'))
init(default_conf, create_engine('sqlite://'))
create_trade(0.001)
create_trade(0.001, int(default_conf['ticker_interval']))
trade = Trade.query.first()
trade.update(limit_buy_order)
assert handle_trade(trade) is True
mocker.patch('freqtrade.main.get_signal', side_effect=lambda s, t: (False, True))
assert handle_trade(trade, int(default_conf['ticker_interval'])) is True

18
freqtrade/tests/test_misc.py
View File

@ -5,10 +5,11 @@ import time
from copy import deepcopy
import pytest
from unittest.mock import MagicMock
from jsonschema import ValidationError
from freqtrade.misc import (common_args_parser, load_config, parse_args,
throttle)
throttle, file_dump_json, parse_timerange)
def test_throttle():
@ -133,6 +134,21 @@ def test_parse_args_hyperopt_custom(mocker):
assert call_args.func is not None
def test_file_dump_json(default_conf, mocker):
file_open = mocker.patch('freqtrade.misc.open', MagicMock())
json_dump = mocker.patch('json.dump', MagicMock())
file_dump_json('somefile', [1, 2, 3])
assert file_open.call_count == 1
assert json_dump.call_count == 1
def test_parse_timerange_incorrect():
assert ((None, 'line'), None, -200) == parse_timerange('-200')
assert (('line', None), 200, None) == parse_timerange('200-')
with pytest.raises(Exception, match=r'Incorrect syntax.*'):
parse_timerange('-')
def test_load_config(default_conf, mocker):
file_mock = mocker.patch('freqtrade.misc.open', mocker.mock_open(
read_data=json.dumps(default_conf)

52
freqtrade/tests/test_persistence.py
View File

@ -1,10 +1,9 @@
# pragma pylint: disable=missing-docstring
import os
import pytest
from sqlalchemy import create_engine
from freqtrade.exchange import Exchanges
from freqtrade.persistence import Trade, init
from freqtrade.persistence import Trade, init, clean_dry_run_db
def test_init_create_session(default_conf, mocker):
@ -310,3 +309,50 @@ def test_calc_profit_percent(limit_buy_order, limit_sell_order):
# Test with a custom fee rate on the close trade
assert trade.calc_profit_percent(fee=0.003) == 0.0614782
def test_clean_dry_run_db(default_conf):
init(default_conf, create_engine('sqlite://'))
# Simulate dry_run entries
trade = Trade(
pair='BTC_ETH',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='dry_run_buy_12345'
)
Trade.session.add(trade)
trade = Trade(
pair='BTC_ETC',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='dry_run_sell_12345'
)
Trade.session.add(trade)
# Simulate prod entry
trade = Trade(
pair='BTC_ETC',
stake_amount=0.001,
amount=123.0,
fee=0.0025,
open_rate=0.123,
exchange='BITTREX',
open_order_id='prod_buy_12345'
)
Trade.session.add(trade)
# We have 3 entries: 2 dry_run, 1 prod
assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 3
clean_dry_run_db()
# We have now only the prod
assert len(Trade.query.filter(Trade.open_order_id.isnot(None)).all()) == 1

1
freqtrade/tests/testdata/BTC_UNITEST-30.json vendored Normal file
View File

File diff suppressed because one or more lines are too long

8
requirements.txt
View File

@ -1,7 +1,7 @@
python-bittrex==0.2.2
SQLAlchemy==1.2.1
SQLAlchemy==1.2.2
python-telegram-bot==9.0.0
arrow==0.12.0
arrow==0.12.1
cachetools==2.0.1
requests==2.18.4
urllib3==1.22
@ -11,7 +11,7 @@ scikit-learn==0.19.1
scipy==1.0.0
jsonschema==2.6.0
numpy==1.14.0
TA-Lib==0.4.15
TA-Lib==0.4.16
pytest==3.3.2
pytest-mock==1.6.3
pytest-cov==2.5.1
@ -19,7 +19,7 @@ hyperopt==0.1
# do not upgrade networkx before this is fixed https://github.com/hyperopt/hyperopt/issues/325
networkx==1.11
tabulate==0.8.2
pymarketcap==3.3.148
pymarketcap==3.3.150
# Required for plotting data
#matplotlib==2.1.0

77
scripts/plot_dataframe.py
View File

@ -1,22 +1,33 @@
#!/usr/bin/env python3
import sys
import argparse
import matplotlib # Install PYQT5 manually if you want to test this helper function
matplotlib.use("Qt5Agg")
import matplotlib.pyplot as plt
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from pandas import DataFrame
from freqtrade import exchange, analyze
from freqtrade.misc import common_args_parser
from freqtrade.strategy.strategy import Strategy
import matplotlib.pyplot as plt
import matplotlib # Install PYQT5 manually if you want to test this helper function
matplotlib.use("Qt5Agg")
def plot_parse_args(args ):
def plot_parse_args(args):
parser = common_args_parser(description='Graph utility')
parser.add_argument(
'-p', '--pair',
help = 'What currency pair',
dest = 'pair',
default = 'BTC_ETH',
type = str,
help='What currency pair',
dest='pair',
default='BTC_ETH',
type=str,
)
parser.add_argument(
'-i', '--interval',
help='what interval to use',
dest='interval',
default=5,
type=int,
)
return parser.parse_args(args)
@ -27,24 +38,25 @@ def plot_analyzed_dataframe(args) -> None:
:param pair: pair as str
:return: None
"""
pair = args.pair
# Init strategy
strategy = Strategy()
strategy.init({'strategy': args.strategy})
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
ticker = exchange.get_ticker_history(pair)
ticker = exchange.get_ticker_history(args.pair, args.interval)
dataframe = analyze.analyze_ticker(ticker)
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
dataframe = populate_indicator(dataframe)
# Two subplots sharing x axis
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle(pair, fontsize=14, fontweight='bold')
fig.suptitle(args.pair + " " + str(args.interval), fontsize=14, fontweight='bold')
ax1.plot(dataframe.index.values, dataframe['close'], label='close')
# ax1.plot(dataframe.index.values, dataframe['sell'], 'ro', label='sell')
ax1.plot(dataframe.index.values, dataframe['sma'], '--', label='SMA')
ax1.plot(dataframe.index.values, dataframe['tema'], ':', label='TEMA')
ax1.plot(dataframe.index.values, dataframe['blower'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['bb_lowerband'], '-.', label='BB low')
ax1.plot(dataframe.index.values, dataframe['buy_price'], 'bo', label='buy')
ax1.legend()
@ -65,6 +77,41 @@ def plot_analyzed_dataframe(args) -> None:
plt.show()
def populate_indicator(dataframe: DataFrame) -> DataFrame:
dataframe.loc[dataframe['buy'] == 1, 'buy_price'] = dataframe['close']
dataframe.loc[dataframe['sell'] == 1, 'sell_price'] = dataframe['close']
# ADX
if 'adx' not in dataframe:
dataframe['adx'] = ta.ADX(dataframe)
# Bollinger bands
if 'bb_lowerband' not in dataframe:
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
# Stoch fast
if 'fastd' not in dataframe or 'fastk' not in dataframe:
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# MFI
if 'mfi' not in dataframe:
dataframe['mfi'] = ta.MFI(dataframe)
# SMA - Simple Moving Average
if 'sma' not in dataframe:
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
# TEMA - Triple Exponential Moving Average
if 'tema' not in dataframe:
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
return dataframe
if __name__ == '__main__':
args = plot_parse_args(sys.argv[1:])
plot_analyzed_dataframe(args)

160
scripts/plot_profit.py Executable file
View File

@ -0,0 +1,160 @@
#!/usr/bin/env python3
import sys
import json
import matplotlib.pyplot as plt
import numpy as np
import freqtrade.optimize as optimize
import freqtrade.misc as misc
from freqtrade.strategy.strategy import Strategy
def plot_parse_args(args):
parser = misc.common_args_parser('Graph utility')
# FIX: perhaps delete those backtesting options that are not feasible (shows up in -h)
misc.backtesting_options(parser)
parser.add_argument(
'-p', '--pair',
help='Show profits for only this pairs. Pairs are comma-separated.',
dest='pair',
default=None
)
return parser.parse_args(args)
# data:: [ pair, profit-%, enter, exit, time, duration]
# data:: ['BTC_XMR', 0.00537847, '1511176800', '1511178000', 5057, 1]
# FIX: make use of the enter/exit dates to insert the
# profit more precisely into the pg array
def make_profit_array(data, px, filter_pairs=[]):
pg = np.zeros(px)
# Go through the trades
# and make an total profit
# array
for trade in data:
pair = trade[0]
if filter_pairs and pair not in filter_pairs:
continue
profit = trade[1]
tim = trade[4]
dur = trade[5]
pg[tim+dur-1] += profit
# rewrite the pg array to go from
# total profits at each timeframe
# to accumulated profits
pa = 0
for x in range(0, len(pg)):
p = pg[x] # Get current total percent
pa += p # Add to the accumulated percent
pg[x] = pa # write back to save memory
return pg
def plot_profit(args) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
# We need to use the same pairs, same tick_interval
# and same timeperiod as used in backtesting
# to match the tickerdata against the profits-results
filter_pairs = args.pair
config = misc.load_config(args.config)
config.update({'strategy': args.strategy})
# Init strategy
strategy = Strategy()
strategy.init(config)
pairs = config['exchange']['pair_whitelist']
if filter_pairs:
filter_pairs = filter_pairs.split(',')
pairs = list(set(pairs) & set(filter_pairs))
print('Filter, keep pairs %s' % pairs)
tickers = optimize.load_data(args.datadir, pairs=pairs,
ticker_interval=strategy.ticker_interval,
refresh_pairs=False)
dataframes = optimize.preprocess(tickers)
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# FIX: since the dataframes are of unequal length,
# andor has different dates, we need to merge them
# But we dont have the date information in the
# backtesting results, this is needed to match the dates
# For now, assume the dataframes are aligned.
max_x = 0
for pair, pair_data in dataframes.items():
n = len(pair_data['close'])
max_x = max(max_x, n)
# if max_x != n:
# raise Exception('Please rerun script. Input data has different lengths %s'
# %('Different pair length: %s <=> %s' %(max_x, n)))
print('max_x: %s' % (max_x))
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
avgclose = np.zeros(max_x)
num = 0
for pair, pair_data in dataframes.items():
close = pair_data['close']
maxprice = max(close) # Normalize price to [0,1]
print('Pair %s has length %s' % (pair, len(close)))
for x in range(0, len(close)):
avgclose[x] += close[x] / maxprice
# avgclose += close
num += 1
avgclose /= num
# Load the profits results
# And make an profits-growth array
filename = 'backtest-result.json'
with open(filename) as file:
data = json.load(file)
pg = make_profit_array(data, max_x, filter_pairs)
#
# Plot the pairs average close prices, and total profit growth
#
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex=True)
fig.suptitle('total profit')
ax1.plot(avgclose, label='avgclose')
ax2.plot(pg, label='profit')
ax1.legend(loc='upper left')
ax2.legend(loc='upper left')
# FIX if we have one line pair in paris
# then skip the plotting of the third graph,
# or change what we plot
# In third graph, we plot each profit separately
for pair in pairs:
pg = make_profit_array(data, max_x, pair)
ax3.plot(pg, label=pair)
ax3.legend(loc='upper left')
# black background to easier see multiple colors
ax3.set_facecolor('black')
# Fine-tune figure; make subplots close to each other and hide x ticks for
# all but bottom plot.
fig.subplots_adjust(hspace=0)
plt.setp([a.get_xticklabels() for a in fig.axes[:-1]], visible=False)
plt.show()
if __name__ == '__main__':
args = plot_parse_args(sys.argv[1:])
plot_profit(args)

0
user_data/data/.gitkeep Normal file
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0
freqtrade/optimize/hyperopt_conf.py → user_data/hyperopt_conf.py
View File

0
user_data/strategies/__init__.py Normal file
View File

246
user_data/strategies/test_strategy.py Normal file
View File

@ -0,0 +1,246 @@
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
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
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = 5
def populate_indicators(self, dataframe: DataFrame) -> 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.
"""
# 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
# ------------------------------------
# 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:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['adx'] > 30) &
(dataframe['tema'] <= dataframe['bb_middleband']) &
(dataframe['tema'] > dataframe['tema'].shift(1))
),
'buy'] = 1
return 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[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
return dataframe