Merge pull request #1229 from mishaker/money_mgt

Edge Positioning
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@ -53,6 +53,21 @@
"sell_profit_only": false, "sell_profit_only": false,
"ignore_roi_if_buy_signal": false "ignore_roi_if_buy_signal": false
}, },
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 7,
"total_capital_in_stake_currency": 0.5,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"telegram": { "telegram": {
"enabled": true, "enabled": true,
"token": "your_telegram_token", "token": "your_telegram_token",

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@ -59,6 +59,20 @@
], ],
"outdated_offset": 5 "outdated_offset": 5
}, },
"edge": {
"enabled": false,
"process_throttle_secs": 3600,
"calculate_since_number_of_days": 2,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"minimum_winrate": 0.60,
"minimum_expectancy": 0.20,
"min_trade_number": 10,
"max_trade_duration_minute": 1440,
"remove_pumps": false
},
"experimental": { "experimental": {
"use_sell_signal": false, "use_sell_signal": false,
"sell_profit_only": false, "sell_profit_only": false,

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@ -47,6 +47,7 @@ The table below will list all configuration parameters.
| `exchange.ccxt_rate_limit` | True | No | DEPRECATED!! Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange. | `exchange.ccxt_rate_limit` | True | No | DEPRECATED!! Have CCXT handle Exchange rate limits. Depending on the exchange, having this to false can lead to temporary bans from the exchange.
| `exchange.ccxt_config` | None | No | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) | `exchange.ccxt_config` | None | No | Additional CCXT parameters passed to the regular ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
| `exchange.ccxt_async_config` | None | No | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation) | `exchange.ccxt_async_config` | None | No | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange and are documented in the [ccxt documentation](https://ccxt.readthedocs.io/en/latest/manual.html#instantiation)
| `edge` | false | No | Please refer to [edge configuration document](edge.md) for detailed explanation.
| `experimental.use_sell_signal` | false | No | Use your sell strategy in addition of the `minimal_roi`. | `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. | `experimental.sell_profit_only` | false | No | waits until you have made a positive profit before taking a sell decision.
| `experimental.ignore_roi_if_buy_signal` | false | No | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal` | `experimental.ignore_roi_if_buy_signal` | false | No | Does not sell if the buy-signal is still active. Takes preference over `minimal_roi` and `use_sell_signal`

151
docs/edge.md Normal file
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@ -0,0 +1,151 @@
# Edge positioning
This page explains how to use Edge Positioning module in your bot in order to enter into a trade only if the trade has a reasonable win rate and risk reward ratio, and consequently adjust your position size and stoploss.
**NOTICE:** Edge positioning is not compatible with dynamic whitelist. it overrides dynamic whitelist.
## Table of Contents
- [Introduction](#introduction)
- [How does it work?](#how-does-it-work?)
- [Configurations](#configurations)
## Introduction
Trading is all about probability. No one can claim that he has a strategy working all the time. You have to assume that sometimes you lose.<br/><br/>
But it doesn't mean there is no rule, it only means rules should work "most of the time". Let's play a game: we toss a coin, heads: I give you 10$, tails: You give me 10$. Is it an interesting game ? no, it is quite boring, isn't it?<br/><br/>
But let's say the probability that we have heads is 80%, and the probability that we have tails is 20%. Now it is becoming interesting ...
That means 10$ x 80% versus 10$ x 20%. 8$ versus 2$. That means over time you will win 8$ risking only 2$ on each toss of coin.<br/><br/>
Let's complicate it more: you win 80% of the time but only 2$, I win 20% of the time but 8$. The calculation is: 80% * 2$ versus 20% * 8$. It is becoming boring again because overtime you win $1.6$ (80% x 2$) and me $1.6 (20% * 8$) too.<br/><br/>
The question is: How do you calculate that? how do you know if you wanna play?
The answer comes to two factors:
- Win Rate
- Risk Reward Ratio
### Win Rate
Means over X trades what is the percentage of winning trades to total number of trades (note that we don't consider how much you gained but only If you won or not).
`W = (Number of winning trades) / (Number of losing trades)`
### Risk Reward Ratio
Risk Reward Ratio is a formula used to measure the expected gains of a given investment against the risk of loss. It is basically what you potentially win divided by what you potentially lose:
`R = Profit / Loss`
Over time, on many trades, you can calculate your risk reward by dividing your average profit on winning trades by your average loss on losing trades:
`Average profit = (Sum of profits) / (Number of winning trades)`
`Average loss = (Sum of losses) / (Number of losing trades)`
`R = (Average profit) / (Average loss)`
### Expectancy
At this point we can combine W and R to create an expectancy ratio. This is a simple process of multiplying the risk reward ratio by the percentage of winning trades, and subtracting the percentage of losing trades, which is calculated as follows:
Expectancy Ratio = (Risk Reward Ratio x Win Rate) Loss Rate
So lets say your Win rate is 28% and your Risk Reward Ratio is 5:
`Expectancy = (5 * 0.28) - 0.72 = 0.68`
Superficially, this means that on average you expect this strategys trades to return .68 times the size of your losers. This is important for two reasons: First, it may seem obvious, but you know right away that you have a positive return. Second, you now have a number you can compare to other candidate systems to make decisions about which ones you employ.
It is important to remember that any system with an expectancy greater than 0 is profitable using past data. The key is finding one that will be profitable in the future.
You can also use this number to evaluate the effectiveness of modifications to this system.
**NOTICE:** It's important to keep in mind that Edge is testing your expectancy using historical data , there's no guarantee that you will have a similar edge in the future. It's still vital to do this testing in order to build confidence in your methodology, but be wary of "curve-fitting" your approach to the historical data as things are unlikely to play out the exact same way for future trades.
## How does it work?
If enabled in config, Edge will go through historical data with a range of stoplosses in order to find buy and sell/stoploss signals. It then calculates win rate and expectancy over X trades for each stoploss. Here is an example:
| Pair | Stoploss | Win Rate | Risk Reward Ratio | Expectancy |
|----------|:-------------:|-------------:|------------------:|-----------:|
| XZC/ETH | -0.03 | 0.52 |1.359670 | 0.228 |
| XZC/ETH | -0.01 | 0.50 |1.176384 | 0.088 |
| XZC/ETH | -0.02 | 0.51 |1.115941 | 0.079 |
The goal here is to find the best stoploss for the strategy in order to have the maximum expectancy. In the above example stoploss at 3% leads to the maximum expectancy according to historical data.
Edge then forces stoploss to your strategy dynamically.
### Position size
Edge dictates the stake amount for each trade to the bot according to the following factors:
- Allowed capital at risk
- Stoploss
Allowed capital at risk is calculated as follows:
**allowed capital at risk** = **total capital** X **allowed risk per trade**
**total capital** is your stake amount.
**Stoploss** is calculated as described above against historical data.
Your position size then will be:
**position size** = **allowed capital at risk** / **stoploss**
Example:
Let's say your stake amount is 3 ETH, you would allow 1% of risk for each trade. thus your allowed capital at risk would be **3 x 0.01 = 0.03 ETH**. Let's assume Edge has calculated that for **XLM/ETH** market your stoploss should be at 2%. So your position size will be **0.03 / 0.02= 1.5ETH**.<br/>
## Configurations
Edge has following configurations:
#### enabled
If true, then Edge will run periodically<br/>
(default to false)
#### process_throttle_secs
How often should Edge run in seconds? <br/>
(default to 3600 so one hour)
#### calculate_since_number_of_days
Number of days of data against which Edge calculates Win Rate, Risk Reward and Expectancy
Note that it downloads historical data so increasing this number would lead to slowing down the bot<br/>
(default to 7)
#### allowed_risk
Percentage of allowed risk per trade<br/>
(default to 0.01 [1%])
#### stoploss_range_min
Minimum stoploss <br/>
(default to -0.01)
#### stoploss_range_max
Maximum stoploss <br/>
(default to -0.10)
#### stoploss_range_step
As an example if this is set to -0.01 then Edge will test the strategy for [-0.01, -0,02, -0,03 ..., -0.09, -0.10] ranges.
Note than having a smaller step means having a bigger range which could lead to slow calculation. <br/>
if you set this parameter to -0.001, you then slow down the Edge calculation by a factor of 10. <br/>
(default to -0.01)
#### minimum_winrate
It filters pairs which don't have at least minimum_winrate.
This comes handy if you want to be conservative and don't comprise win rate in favor of risk reward ratio.<br/>
(default to 0.60)
#### minimum_expectancy
It filters paris which have an expectancy lower than this number .
Having an expectancy of 0.20 means if you put 10$ on a trade you expect a 12$ return.<br/>
(default to 0.20)
#### min_trade_number
When calculating W and R and E (expectancy) against historical data, you always want to have a minimum number of trades. The more this number is the more Edge is reliable. Having a win rate of 100% on a single trade doesn't mean anything at all. But having a win rate of 70% over past 100 trades means clearly something. <br/>
(default to 10, it is highly recommended not to decrease this number)
#### max_trade_duration_minute
Edge will filter out trades with long duration. If a trade is profitable after 1 month, it is hard to evaluate the strategy based on it. But if most of trades are profitable and they have maximum duration of 30 minutes, then it is clearly a good sign.<br/>
**NOTICE:** While configuring this value, you should take into consideration your ticker interval. as an example filtering out trades having duration less than one day for a strategy which has 4h interval does not make sense. default value is set assuming your strategy interval is relatively small (1m or 5m, etc).<br/>
(default to 1 day, 1440 = 60 * 24)
#### remove_pumps
Edge will remove sudden pumps in a given market while going through historical data. However, given that pumps happen very often in crypto markets, we recommend you keep this off.<br/>
(default to false)

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@ -25,6 +25,7 @@ Pull-request. Do not hesitate to reach us on
- [Change your strategy](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy) - [Change your strategy](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#change-your-strategy)
- [Add more Indicator](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator) - [Add more Indicator](https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md#add-more-indicator)
- [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md) - [Test your strategy with Backtesting](https://github.com/freqtrade/freqtrade/blob/develop/docs/backtesting.md)
- [Edge positioning](https://github.com/mishaker/freqtrade/blob/money_mgt/docs/edge.md)
- [Find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md) - [Find optimal parameters with Hyperopt](https://github.com/freqtrade/freqtrade/blob/develop/docs/hyperopt.md)
- [Control the bot with telegram](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md) - [Control the bot with telegram](https://github.com/freqtrade/freqtrade/blob/develop/docs/telegram-usage.md)
- [Receive notifications via webhook](https://github.com/freqtrade/freqtrade/blob/develop/docs/webhook-config.md) - [Receive notifications via webhook](https://github.com/freqtrade/freqtrade/blob/develop/docs/webhook-config.md)

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@ -102,6 +102,7 @@ CONF_SCHEMA = {
} }
}, },
'exchange': {'$ref': '#/definitions/exchange'}, 'exchange': {'$ref': '#/definitions/exchange'},
'edge': {'$ref': '#/definitions/edge'},
'experimental': { 'experimental': {
'type': 'object', 'type': 'object',
'properties': { 'properties': {
@ -170,6 +171,23 @@ CONF_SCHEMA = {
'ccxt_async_config': {'type': 'object'} 'ccxt_async_config': {'type': 'object'}
}, },
'required': ['name', 'key', 'secret', 'pair_whitelist'] 'required': ['name', 'key', 'secret', 'pair_whitelist']
},
'edge': {
'type': 'object',
'properties': {
"enabled": {'type': 'boolean'},
"process_throttle_secs": {'type': 'integer', 'minimum': 600},
"calculate_since_number_of_days": {'type': 'integer'},
"allowed_risk": {'type': 'number'},
"stoploss_range_min": {'type': 'number'},
"stoploss_range_max": {'type': 'number'},
"stoploss_range_step": {'type': 'number'},
"minimum_winrate": {'type': 'number'},
"minimum_expectancy": {'type': 'number'},
"min_trade_number": {'type': 'number'},
"max_trade_duration_minute": {'type': 'integer'},
"remove_pumps": {'type': 'boolean'}
}
} }
}, },
'anyOf': [ 'anyOf': [

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freqtrade/edge/__init__.py Normal file
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# pragma pylint: disable=W0603
""" Edge positioning package """
import logging
from typing import Any, Dict
from collections import namedtuple
import arrow
import numpy as np
import utils_find_1st as utf1st
from pandas import DataFrame
import freqtrade.optimize as optimize
from freqtrade.arguments import Arguments
from freqtrade.arguments import TimeRange
from freqtrade.strategy.interface import SellType
logger = logging.getLogger(__name__)
class Edge():
"""
Calculates Win Rate, Risk Reward Ratio, Expectancy
against historical data for a give set of markets and a strategy
it then adjusts stoploss and position size accordingly
and force it into the strategy
Author: https://github.com/mishaker
"""
config: Dict = {}
_cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
# pair info data type
_pair_info = namedtuple(
'pair_info',
['stoploss', 'winrate', 'risk_reward_ratio', 'required_risk_reward', 'expectancy'])
def __init__(self, config: Dict[str, Any], exchange, strategy) -> None:
self.config = config
self.exchange = exchange
self.strategy = strategy
self.ticker_interval = self.strategy.ticker_interval
self.tickerdata_to_dataframe = self.strategy.tickerdata_to_dataframe
self.get_timeframe = optimize.get_timeframe
self.advise_sell = self.strategy.advise_sell
self.advise_buy = self.strategy.advise_buy
self.edge_config = self.config.get('edge', {})
self._cached_pairs: Dict[str, Any] = {} # Keeps a list of pairs
self._total_capital: float = self.config['stake_amount']
self._allowed_risk: float = self.edge_config.get('allowed_risk')
self._since_number_of_days: int = self.edge_config.get('calculate_since_number_of_days', 14)
self._last_updated: int = 0 # Timestamp of pairs last updated time
self._stoploss_range_min = float(self.edge_config.get('stoploss_range_min', -0.01))
self._stoploss_range_max = float(self.edge_config.get('stoploss_range_max', -0.05))
self._stoploss_range_step = float(self.edge_config.get('stoploss_range_step', -0.001))
# calculating stoploss range
self._stoploss_range = np.arange(
self._stoploss_range_min,
self._stoploss_range_max,
self._stoploss_range_step
)
self._timerange: TimeRange = Arguments.parse_timerange("%s-" % arrow.now().shift(
days=-1 * self._since_number_of_days).format('YYYYMMDD'))
self.fee = self.exchange.get_fee()
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=True,
exchange=self.exchange,
timerange=self._timerange
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(ticker_data, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
# Not a nice hack but probably simplest solution:
# When backtest load data it loads the delta between disk and exchange
# The problem is that exchange consider that recent.
# it is but it is incomplete (c.f. _async_get_candle_history)
# So it causes get_signal to exit cause incomplete ticker_hist
# A patch to that would be update _pairs_last_refresh_time of exchange
# so it will download again all pairs
# Another solution is to add new data to klines instead of reassigning it:
# self.klines[pair].update(data) instead of self.klines[pair] = data in exchange package.
# But that means indexing timestamp and having a verification so that
# there is no empty range between two timestaps (recently added and last
# one)
self.exchange._pairs_last_refresh_time = {}
return True
def stake_amount(self, pair: str) -> float:
stoploss = self._cached_pairs[pair].stoploss
allowed_capital_at_risk = round(self._total_capital * self._allowed_risk, 5)
position_size = abs(round((allowed_capital_at_risk / stoploss), 5))
return position_size
def stoploss(self, pair: str) -> float:
return self._cached_pairs[pair].stoploss
def adjust(self, pairs) -> list:
"""
Filters out and sorts "pairs" according to Edge calculated pairs
"""
final = []
for pair, info in self._cached_pairs.items():
if info.expectancy > float(self.edge_config.get('minimum_expectancy', 0.2)) and \
info.winrate > float(self.edge_config.get('minimum_winrate', 0.60)) and \
pair in pairs:
final.append(pair)
if final:
logger.info('Edge validated only %s', final)
else:
logger.info('Edge removed all pairs as no pair with minimum expectancy was found !')
return final
def _fill_calculable_fields(self, result: DataFrame) -> DataFrame:
"""
The result frame contains a number of columns that are calculable
from other columns. These are left blank till all rows are added,
to be populated in single vector calls.
Columns to be populated are:
- Profit
- trade duration
- profit abs
:param result Dataframe
:return: result Dataframe
"""
# stake and fees
# stake = 0.015
# 0.05% is 0.0005
# fee = 0.001
stake = self.config.get('stake_amount')
fee = self.fee
open_fee = fee / 2
close_fee = fee / 2
result['trade_duration'] = result['close_time'] - result['open_time']
result['trade_duration'] = result['trade_duration'].map(
lambda x: int(x.total_seconds() / 60))
# Spends, Takes, Profit, Absolute Profit
# Buy Price
result['buy_vol'] = stake / result['open_rate'] # How many target are we buying
result['buy_fee'] = stake * open_fee
result['buy_spend'] = stake + result['buy_fee'] # How much we're spending
# Sell price
result['sell_sum'] = result['buy_vol'] * result['close_rate']
result['sell_fee'] = result['sell_sum'] * close_fee
result['sell_take'] = result['sell_sum'] - result['sell_fee']
# profit_percent
result['profit_percent'] = (result['sell_take'] - result['buy_spend']) / result['buy_spend']
# Absolute profit
result['profit_abs'] = result['sell_take'] - result['buy_spend']
return result
def _process_expectancy(self, results: DataFrame) -> Dict[str, Any]:
"""
This calculates WinRate, Required Risk Reward, Risk Reward and Expectancy of all pairs
The calulation will be done per pair and per strategy.
"""
# Removing pairs having less than min_trades_number
min_trades_number = self.edge_config.get('min_trade_number', 10)
results = results.groupby(['pair', 'stoploss']).filter(lambda x: len(x) > min_trades_number)
###################################
# Removing outliers (Only Pumps) from the dataset
# The method to detect outliers is to calculate standard deviation
# Then every value more than (standard deviation + 2*average) is out (pump)
#
# Removing Pumps
if self.edge_config.get('remove_pumps', False):
results = results.groupby(['pair', 'stoploss']).apply(
lambda x: x[x['profit_abs'] < 2 * x['profit_abs'].std() + x['profit_abs'].mean()])
##########################################################################
# Removing trades having a duration more than X minutes (set in config)
max_trade_duration = self.edge_config.get('max_trade_duration_minute', 1440)
results = results[results.trade_duration < max_trade_duration]
#######################################################################
if results.empty:
return {}
groupby_aggregator = {
'profit_abs': [
('nb_trades', 'count'), # number of all trades
('profit_sum', lambda x: x[x > 0].sum()), # cumulative profit of all winning trades
('loss_sum', lambda x: abs(x[x < 0].sum())), # cumulative loss of all losing trades
('nb_win_trades', lambda x: x[x > 0].count()) # number of winning trades
],
'trade_duration': [('avg_trade_duration', 'mean')]
}
# Group by (pair and stoploss) by applying above aggregator
df = results.groupby(['pair', 'stoploss'])['profit_abs', 'trade_duration'].agg(
groupby_aggregator).reset_index(col_level=1)
# Dropping level 0 as we don't need it
df.columns = df.columns.droplevel(0)
# Calculating number of losing trades, average win and average loss
df['nb_loss_trades'] = df['nb_trades'] - df['nb_win_trades']
df['average_win'] = df['profit_sum'] / df['nb_win_trades']
df['average_loss'] = df['loss_sum'] / df['nb_loss_trades']
# Win rate = number of profitable trades / number of trades
df['winrate'] = df['nb_win_trades'] / df['nb_trades']
# risk_reward_ratio = average win / average loss
df['risk_reward_ratio'] = df['average_win'] / df['average_loss']
# required_risk_reward = (1 / winrate) - 1
df['required_risk_reward'] = (1 / df['winrate']) - 1
# expectancy = (risk_reward_ratio * winrate) - (lossrate)
df['expectancy'] = (df['risk_reward_ratio'] * df['winrate']) - (1 - df['winrate'])
# sort by expectancy and stoploss
df = df.sort_values(by=['expectancy', 'stoploss'], ascending=False).groupby(
'pair').first().sort_values(by=['expectancy'], ascending=False).reset_index()
final = {}
for x in df.itertuples():
info = {
'stoploss': x.stoploss,
'winrate': x.winrate,
'risk_reward_ratio': x.risk_reward_ratio,
'required_risk_reward': x.required_risk_reward,
'expectancy': x.expectancy
}
final[x.pair] = self._pair_info(**info)
# Returning a list of pairs in order of "expectancy"
return final
def _find_trades_for_stoploss_range(self, ticker_data, pair, stoploss_range):
buy_column = ticker_data['buy'].values
sell_column = ticker_data['sell'].values
date_column = ticker_data['date'].values
ohlc_columns = ticker_data[['open', 'high', 'low', 'close']].values
result: list = []
for stoploss in stoploss_range:
result += self._detect_next_stop_or_sell_point(
buy_column, sell_column, date_column, ohlc_columns, round(stoploss, 6), pair
)
return result
def _detect_next_stop_or_sell_point(self, buy_column, sell_column, date_column,
ohlc_columns, stoploss, pair, start_point=0):
"""
Iterate through ohlc_columns recursively in order to find the next trade
Next trade opens from the first buy signal noticed to
The sell or stoploss signal after it.
It then calls itself cutting OHLC, buy_column, sell_colum and date_column
Cut from (the exit trade index) + 1
Author: https://github.com/mishaker
"""
result: list = []
open_trade_index = utf1st.find_1st(buy_column, 1, utf1st.cmp_equal)
# return empty if we don't find trade entry (i.e. buy==1) or
# we find a buy but at the of array
if open_trade_index == -1 or open_trade_index == len(buy_column) - 1:
return []
else:
open_trade_index += 1 # when a buy signal is seen,
# trade opens in reality on the next candle
stop_price_percentage = stoploss + 1
open_price = ohlc_columns[open_trade_index, 0]
stop_price = (open_price * stop_price_percentage)
# Searching for the index where stoploss is hit
stop_index = utf1st.find_1st(
ohlc_columns[open_trade_index:, 2], stop_price, utf1st.cmp_smaller)
# If we don't find it then we assume stop_index will be far in future (infinite number)
if stop_index == -1:
stop_index = float('inf')
# Searching for the index where sell is hit
sell_index = utf1st.find_1st(sell_column[open_trade_index:], 1, utf1st.cmp_equal)
# If we don't find it then we assume sell_index will be far in future (infinite number)
if sell_index == -1:
sell_index = float('inf')
# Check if we don't find any stop or sell point (in that case trade remains open)
# It is not interesting for Edge to consider it so we simply ignore the trade
# And stop iterating there is no more entry
if stop_index == sell_index == float('inf'):
return []
if stop_index <= sell_index:
exit_index = open_trade_index + stop_index
exit_type = SellType.STOP_LOSS
exit_price = stop_price
elif stop_index > sell_index:
# if exit is SELL then we exit at the next candle
exit_index = open_trade_index + sell_index + 1
# check if we have the next candle
if len(ohlc_columns) - 1 < exit_index:
return []
exit_type = SellType.SELL_SIGNAL
exit_price = ohlc_columns[exit_index, 0]
trade = {'pair': pair,
'stoploss': stoploss,
'profit_percent': '',
'profit_abs': '',
'open_time': date_column[open_trade_index],
'close_time': date_column[exit_index],
'open_index': start_point + open_trade_index,
'close_index': start_point + exit_index,
'trade_duration': '',
'open_rate': round(open_price, 15),
'close_rate': round(exit_price, 15),
'exit_type': exit_type
}
result.append(trade)
# Calling again the same function recursively but giving
# it a view of exit_index till the end of array
return result + self._detect_next_stop_or_sell_point(
buy_column[exit_index:],
sell_column[exit_index:],
date_column[exit_index:],
ohlc_columns[exit_index:],
stoploss,
pair,
(start_point + exit_index)
)

View File

@ -17,6 +17,7 @@ from cachetools import TTLCache, cached
from freqtrade import (DependencyException, OperationalException, from freqtrade import (DependencyException, OperationalException,
TemporaryError, __version__, constants, persistence) TemporaryError, __version__, constants, persistence)
from freqtrade.exchange import Exchange from freqtrade.exchange import Exchange
from freqtrade.edge import Edge
from freqtrade.persistence import Trade from freqtrade.persistence import Trade
from freqtrade.rpc import RPCManager, RPCMessageType from freqtrade.rpc import RPCManager, RPCMessageType
from freqtrade.state import State from freqtrade.state import State
@ -24,6 +25,7 @@ from freqtrade.strategy.interface import SellType
from freqtrade.strategy.resolver import IStrategy, StrategyResolver from freqtrade.strategy.resolver import IStrategy, StrategyResolver
from freqtrade.exchange.exchange_helpers import order_book_to_dataframe from freqtrade.exchange.exchange_helpers import order_book_to_dataframe
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -54,6 +56,11 @@ class FreqtradeBot(object):
self.rpc: RPCManager = RPCManager(self) self.rpc: RPCManager = RPCManager(self)
self.persistence = None self.persistence = None
self.exchange = Exchange(self.config) self.exchange = Exchange(self.config)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist'] self.active_pair_whitelist: List[str] = self.config['exchange']['pair_whitelist']
self._init_modules() self._init_modules()
@ -179,6 +186,17 @@ class FreqtradeBot(object):
# Keep only the subsets of pairs wanted (up to nb_assets) # Keep only the subsets of pairs wanted (up to nb_assets)
self.active_pair_whitelist = sanitized_list[:nb_assets] if nb_assets else sanitized_list self.active_pair_whitelist = sanitized_list[:nb_assets] if nb_assets else sanitized_list
# Calculating Edge positiong
# Should be called before refresh_tickers
# Otherwise it will override cached klines in exchange
# with delta value (klines only from last refresh_pairs)
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Refreshing candles
self.exchange.refresh_tickers(self.active_pair_whitelist, self.strategy.ticker_interval)
# Query trades from persistence layer # Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all() trades = Trade.query.filter(Trade.is_open.is_(True)).all()
@ -309,13 +327,17 @@ class FreqtradeBot(object):
return used_rate return used_rate
def _get_trade_stake_amount(self) -> Optional[float]: def _get_trade_stake_amount(self, pair) -> Optional[float]:
""" """
Check if stake amount can be fulfilled with the available balance Check if stake amount can be fulfilled with the available balance
for the stake currency for the stake currency
:return: float: Stake Amount :return: float: Stake Amount
""" """
if self.edge:
stake_amount = self.edge.stake_amount(pair)
else:
stake_amount = self.config['stake_amount'] stake_amount = self.config['stake_amount']
avaliable_amount = self.exchange.get_balance(self.config['stake_currency']) avaliable_amount = self.exchange.get_balance(self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT: if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
@ -373,15 +395,6 @@ class FreqtradeBot(object):
:return: True if a trade object has been created and persisted, False otherwise :return: True if a trade object has been created and persisted, False otherwise
""" """
interval = self.strategy.ticker_interval interval = self.strategy.ticker_interval
stake_amount = self._get_trade_stake_amount()
if not stake_amount:
return False
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount
)
whitelist = copy.deepcopy(self.active_pair_whitelist) whitelist = copy.deepcopy(self.active_pair_whitelist)
# Remove currently opened and latest pairs from whitelist # Remove currently opened and latest pairs from whitelist
@ -394,10 +407,18 @@ class FreqtradeBot(object):
raise DependencyException('No currency pairs in whitelist') raise DependencyException('No currency pairs in whitelist')
# running get_signal on historical data fetched # running get_signal on historical data fetched
# to find buy signals
for _pair in whitelist: for _pair in whitelist:
(buy, sell) = self.strategy.get_signal(_pair, interval, self.exchange.klines.get(_pair)) (buy, sell) = self.strategy.get_signal(_pair, interval, self.exchange.klines.get(_pair))
if buy and not sell: if buy and not sell:
stake_amount = self._get_trade_stake_amount(_pair)
if not stake_amount:
return False
logger.info(
'Buy signal found: about create a new trade with stake_amount: %f ...',
stake_amount
)
bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\ bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\
get('check_depth_of_market', {}) get('check_depth_of_market', {})
if (bidstrat_check_depth_of_market.get('enabled', False)) and\ if (bidstrat_check_depth_of_market.get('enabled', False)) and\
@ -624,10 +645,16 @@ class FreqtradeBot(object):
return False return False
def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool: def check_sell(self, trade: Trade, sell_rate: float, buy: bool, sell: bool) -> bool:
if self.edge:
stoploss = self.edge.stoploss(trade.pair)
should_sell = self.strategy.should_sell(
trade, sell_rate, datetime.utcnow(), buy, sell, force_stoploss=stoploss)
else:
should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell) should_sell = self.strategy.should_sell(trade, sell_rate, datetime.utcnow(), buy, sell)
if should_sell.sell_flag: if should_sell.sell_flag:
self.execute_sell(trade, sell_rate, should_sell.sell_type) self.execute_sell(trade, sell_rate, should_sell.sell_type)
logger.info('excuted sell') logger.info('executed sell, reason: %s', should_sell.sell_type)
return True return True
return False return False

View File

@ -410,7 +410,7 @@ class RPC(object):
raise RPCException(f'position for {pair} already open - id: {trade.id}') raise RPCException(f'position for {pair} already open - id: {trade.id}')
# gen stake amount # gen stake amount
stakeamount = self._freqtrade._get_trade_stake_amount() stakeamount = self._freqtrade._get_trade_stake_amount(pair)
# execute buy # execute buy
if self._freqtrade.execute_buy(pair, stakeamount, price): if self._freqtrade.execute_buy(pair, stakeamount, price):

View File

@ -203,17 +203,20 @@ class IStrategy(ABC):
return buy, sell return buy, sell
def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
sell: bool, low: float = None, high: float = None) -> SellCheckTuple: sell: bool, low: float = None, high: float = None,
force_stoploss: float = 0) -> SellCheckTuple:
""" """
This function evaluate if on the condition required to trigger a sell has been reached This function evaluate if on the condition required to trigger a sell has been reached
if the threshold is reached and updates the trade record. if the threshold is reached and updates the trade record.
:return: True if trade should be sold, False otherwise :return: True if trade should be sold, False otherwise
""" """
# Set current rate to low for backtesting sell # Set current rate to low for backtesting sell
current_rate = low or rate current_rate = low or rate
current_profit = trade.calc_profit_percent(current_rate) current_profit = trade.calc_profit_percent(current_rate)
stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade, stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
current_time=date, current_profit=current_profit) current_time=date, current_profit=current_profit,
force_stoploss=force_stoploss)
if stoplossflag.sell_flag: if stoplossflag.sell_flag:
return stoplossflag return stoplossflag
# Set current rate to low for backtesting sell # Set current rate to low for backtesting sell
@ -241,7 +244,7 @@ class IStrategy(ABC):
return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE) return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime, def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
current_profit: float) -> SellCheckTuple: current_profit: float, force_stoploss: float) -> SellCheckTuple:
""" """
Based on current profit of the trade and configured (trailing) stoploss, Based on current profit of the trade and configured (trailing) stoploss,
decides to sell or not decides to sell or not
@ -250,7 +253,8 @@ class IStrategy(ABC):
trailing_stop = self.config.get('trailing_stop', False) trailing_stop = self.config.get('trailing_stop', False)
trade.adjust_stop_loss(trade.open_rate, self.stoploss, initial=True) trade.adjust_stop_loss(trade.open_rate, force_stoploss if force_stoploss
else self.stoploss, initial=True)
# evaluate if the stoploss was hit # evaluate if the stoploss was hit
if self.stoploss is not None and trade.stop_loss >= current_rate: if self.stoploss is not None and trade.stop_loss >= current_rate:

View File

@ -4,6 +4,7 @@ import logging
from datetime import datetime from datetime import datetime
from functools import reduce from functools import reduce
from typing import Dict, Optional from typing import Dict, Optional
from collections import namedtuple
from unittest.mock import MagicMock, PropertyMock from unittest.mock import MagicMock, PropertyMock
import arrow import arrow
@ -12,6 +13,7 @@ from telegram import Chat, Message, Update
from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
from freqtrade.exchange import Exchange from freqtrade.exchange import Exchange
from freqtrade.edge import Edge
from freqtrade.freqtradebot import FreqtradeBot from freqtrade.freqtradebot import FreqtradeBot
logging.getLogger('').setLevel(logging.INFO) logging.getLogger('').setLevel(logging.INFO)
@ -42,7 +44,32 @@ def get_patched_exchange(mocker, config, api_mock=None) -> Exchange:
return exchange return exchange
def patch_edge(mocker) -> None:
# "ETH/BTC",
# "LTC/BTC",
# "XRP/BTC",
# "NEO/BTC"
pair_info = namedtuple(
'pair_info',
'stoploss, winrate, risk_reward_ratio, required_risk_reward, expectancy')
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'NEO/BTC': pair_info(-0.20, 0.66, 3.71, 0.50, 1.71),
'LTC/BTC': pair_info(-0.21, 0.66, 3.71, 0.50, 1.71),
}
))
mocker.patch('freqtrade.edge.Edge.stoploss', MagicMock(return_value=-0.20))
mocker.patch('freqtrade.edge.Edge.calculate', MagicMock(return_value=True))
def get_patched_edge(mocker, config) -> Edge:
patch_edge(mocker)
edge = Edge(config)
return edge
# Functions for recurrent object patching # Functions for recurrent object patching
def get_patched_freqtradebot(mocker, config) -> FreqtradeBot: def get_patched_freqtradebot(mocker, config) -> FreqtradeBot:
""" """
This function patch _init_modules() to not call dependencies This function patch _init_modules() to not call dependencies
@ -752,3 +779,23 @@ def buy_order_fee():
'status': 'closed', 'status': 'closed',
'fee': None 'fee': None
} }
@pytest.fixture(scope="function")
def edge_conf(default_conf):
default_conf['edge'] = {
"enabled": True,
"process_throttle_secs": 1800,
"calculate_since_number_of_days": 14,
"allowed_risk": 0.01,
"stoploss_range_min": -0.01,
"stoploss_range_max": -0.1,
"stoploss_range_step": -0.01,
"maximum_winrate": 0.80,
"minimum_expectancy": 0.20,
"min_trade_number": 15,
"max_trade_duration_minute": 1440,
"remove_pumps": False
}
return default_conf

View File

View File

@ -0,0 +1,310 @@
# pragma pylint: disable=missing-docstring, C0103, C0330
# pragma pylint: disable=protected-access, too-many-lines, invalid-name, too-many-arguments
import pytest
import logging
from freqtrade.tests.conftest import get_patched_freqtradebot
from freqtrade.edge import Edge
from pandas import DataFrame, to_datetime
from freqtrade.strategy.interface import SellType
from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
_get_frame_time_from_offset)
import arrow
import numpy as np
import math
from unittest.mock import MagicMock
# Cases to be tested:
# 1) Open trade should be removed from the end
# 2) Two complete trades within dataframe (with sell hit for all)
# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
# 4) Entered, sl 3%, candle drops 4%, recovers to 1% => Trade closed, 3% loss
# 5) Stoploss and sell are hit. should sell on stoploss
####################################################################
ticker_start_time = arrow.get(2018, 10, 3)
ticker_interval_in_minute = 60
_ohlc = {'date': 0, 'buy': 1, 'open': 2, 'high': 3, 'low': 4, 'close': 5, 'sell': 6, 'volume': 7}
# Open trade should be removed from the end
tc0 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 1]], # enter trade (signal on last candle)
stop_loss=-0.99, roi=float('inf'), profit_perc=0.00,
trades=[]
)
# Two complete trades within dataframe(with sell hit for all)
tc1 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4975, 4987, 6172, 0, 1], # enter trade (signal on last candle)
[2, 5000, 5025, 4975, 4987, 6172, 0, 0], # exit at open
[3, 5000, 5025, 4975, 4987, 6172, 1, 0], # no action
[4, 5000, 5025, 4975, 4987, 6172, 0, 0], # should enter the trade
[5, 5000, 5025, 4975, 4987, 6172, 0, 1], # no action
[6, 5000, 5025, 4975, 4987, 6172, 0, 0], # should sell
],
stop_loss=-0.99, roi=float('inf'), profit_perc=0.00,
trades=[BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=1, close_tick=2),
BTrade(sell_reason=SellType.SELL_SIGNAL, open_tick=4, close_tick=6)]
)
# 3) Entered, sl 1%, candle drops 8% => Trade closed, 1% loss
tc2 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4600, 4987, 6172, 0, 0], # enter trade, stoploss hit
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.01, roi=float('inf'), profit_perc=-0.01,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
# 4) Entered, sl 3 %, candle drops 4%, recovers to 1 % = > Trade closed, 3 % loss
tc3 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4800, 4987, 6172, 0, 0], # enter trade, stoploss hit
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.03, roi=float('inf'), profit_perc=-0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
# 5) Stoploss and sell are hit. should sell on stoploss
tc4 = BTContainer(data=[
# D O H L C V B S
[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
[1, 5000, 5025, 4800, 4987, 6172, 0, 1], # enter trade, stoploss hit, sell signal
[2, 5000, 5025, 4975, 4987, 6172, 0, 0],
],
stop_loss=-0.03, roi=float('inf'), profit_perc=-0.03,
trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=1)]
)
TESTS = [
tc0,
tc1,
tc2,
tc3,
tc4
]
@pytest.mark.parametrize("data", TESTS)
def test_edge_results(edge_conf, mocker, caplog, data) -> None:
"""
run functional tests
"""
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
frame = _build_backtest_dataframe(data.data)
caplog.set_level(logging.DEBUG)
edge.fee = 0
trades = edge._find_trades_for_stoploss_range(frame, 'TEST/BTC', [data.stop_loss])
results = edge._fill_calculable_fields(DataFrame(trades)) if trades else DataFrame()
print(results)
assert len(trades) == len(data.trades)
if not results.empty:
assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.exit_type == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
def test_adjust(mocker, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
edge = Edge(default_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71),
'C/D': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71),
'N/O': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71)
}
))
pairs = ['A/B', 'C/D', 'E/F', 'G/H']
assert(edge.adjust(pairs) == ['E/F', 'C/D'])
def test_stoploss(mocker, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
edge = Edge(default_conf, freqtrade.exchange, freqtrade.strategy)
mocker.patch('freqtrade.edge.Edge._cached_pairs', mocker.PropertyMock(
return_value={
'E/F': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71),
'C/D': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71),
'N/O': Edge._pair_info(-0.01, 0.66, 3.71, 0.50, 1.71)
}
))
assert edge.stoploss('E/F') == -0.01
def _validate_ohlc(buy_ohlc_sell_matrice):
for index, ohlc in enumerate(buy_ohlc_sell_matrice):
# if not high < open < low or not high < close < low
if not ohlc[3] >= ohlc[2] >= ohlc[4] or not ohlc[3] >= ohlc[5] >= ohlc[4]:
raise Exception('Line ' + str(index + 1) + ' of ohlc has invalid values!')
return True
def _build_dataframe(buy_ohlc_sell_matrice):
_validate_ohlc(buy_ohlc_sell_matrice)
tickers = []
for ohlc in buy_ohlc_sell_matrice:
ticker = {
'date': ticker_start_time.shift(
minutes=(
ohlc[0] *
ticker_interval_in_minute)).timestamp *
1000,
'buy': ohlc[1],
'open': ohlc[2],
'high': ohlc[3],
'low': ohlc[4],
'close': ohlc[5],
'sell': ohlc[6]}
tickers.append(ticker)
frame = DataFrame(tickers)
frame['date'] = to_datetime(frame['date'],
unit='ms',
utc=True,
infer_datetime_format=True)
return frame
def _time_on_candle(number):
return np.datetime64(ticker_start_time.shift(
minutes=(number * ticker_interval_in_minute)).timestamp * 1000, 'ms')
def test_edge_heartbeat_calculate(mocker, edge_conf):
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
heartbeat = edge_conf['edge']['process_throttle_secs']
# should not recalculate if heartbeat not reached
edge._last_updated = arrow.utcnow().timestamp - heartbeat + 1
assert edge.calculate() is False
def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
timerange=None, exchange=None):
hz = 0.1
base = 0.001
ETHBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
math.sin(x * hz) / 1000 + base,
123.45
] for x in range(0, 500)]
hz = 0.2
base = 0.002
LTCBTC = [
[
ticker_start_time.shift(minutes=(x * ticker_interval_in_minute)).timestamp * 1000,
math.sin(x * hz) / 1000 + base,
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
math.sin(x * hz) / 1000 + base,
123.45
] for x in range(0, 500)]
pairdata = {'NEO/BTC': ETHBTC, 'LTC/BTC': LTCBTC}
return pairdata
def test_edge_process_downloaded_data(mocker, default_conf):
default_conf['datadir'] = None
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.001))
mocker.patch('freqtrade.optimize.load_data', mocked_load_data)
edge = Edge(default_conf, freqtrade.exchange, freqtrade.strategy)
assert edge.calculate()
assert len(edge._cached_pairs) == 2
assert edge._last_updated <= arrow.utcnow().timestamp + 2
def test_process_expectancy(mocker, edge_conf):
edge_conf['edge']['min_trade_number'] = 2
freqtrade = get_patched_freqtradebot(mocker, edge_conf)
def get_fee():
return 0.001
freqtrade.exchange.get_fee = get_fee
edge = Edge(edge_conf, freqtrade.exchange, freqtrade.strategy)
trades = [
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:05:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:10:00.000000000'),
'open_index': 1,
'close_index': 1,
'trade_duration': '',
'open_rate': 17,
'close_rate': 17,
'exit_type': 'sell_signal'},
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:20:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:25:00.000000000'),
'open_index': 4,
'close_index': 4,
'trade_duration': '',
'open_rate': 20,
'close_rate': 20,
'exit_type': 'sell_signal'},
{'pair': 'TEST/BTC',
'stoploss': -0.9,
'profit_percent': '',
'profit_abs': '',
'open_time': np.datetime64('2018-10-03T00:30:00.000000000'),
'close_time': np.datetime64('2018-10-03T00:40:00.000000000'),
'open_index': 6,
'close_index': 7,
'trade_duration': '',
'open_rate': 26,
'close_rate': 34,
'exit_type': 'sell_signal'}
]
trades_df = DataFrame(trades)
trades_df = edge._fill_calculable_fields(trades_df)
final = edge._process_expectancy(trades_df)
assert len(final) == 1
assert 'TEST/BTC' in final
assert final['TEST/BTC'].stoploss == -0.9
assert round(final['TEST/BTC'].winrate, 10) == 0.3333333333
assert round(final['TEST/BTC'].risk_reward_ratio, 10) == 306.5384615384
assert round(final['TEST/BTC'].required_risk_reward, 10) == 2.0
assert round(final['TEST/BTC'].expectancy, 10) == 101.5128205128

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@ -31,7 +31,7 @@ class BTContainer(NamedTuple):
def _get_frame_time_from_offset(offset): def _get_frame_time_from_offset(offset):
return ticker_start_time.shift( return ticker_start_time.shift(
minutes=(offset * ticker_interval_in_minute)).datetime minutes=(offset * ticker_interval_in_minute)).datetime.replace(tzinfo=None)
def _build_backtest_dataframe(ticker_with_signals): def _build_backtest_dataframe(ticker_with_signals):

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@ -1,4 +1,4 @@
# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument # pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, C0330, unused-argument
import logging import logging
from unittest.mock import MagicMock from unittest.mock import MagicMock

View File

@ -18,7 +18,7 @@ from freqtrade.persistence import Trade
from freqtrade.rpc import RPCMessageType from freqtrade.rpc import RPCMessageType
from freqtrade.state import State from freqtrade.state import State
from freqtrade.strategy.interface import SellType, SellCheckTuple from freqtrade.strategy.interface import SellType, SellCheckTuple
from freqtrade.tests.conftest import log_has, patch_exchange from freqtrade.tests.conftest import log_has, patch_exchange, patch_edge
# Functions for recurrent object patching # Functions for recurrent object patching
@ -177,7 +177,7 @@ def test_get_trade_stake_amount(default_conf, ticker, limit_buy_order, fee, mock
freqtrade = FreqtradeBot(default_conf) freqtrade = FreqtradeBot(default_conf)
result = freqtrade._get_trade_stake_amount() result = freqtrade._get_trade_stake_amount('ETH/BTC')
assert result == default_conf['stake_amount'] assert result == default_conf['stake_amount']
@ -195,7 +195,7 @@ def test_get_trade_stake_amount_no_stake_amount(default_conf,
freqtrade = FreqtradeBot(default_conf) freqtrade = FreqtradeBot(default_conf)
with pytest.raises(DependencyException, match=r'.*stake amount.*'): with pytest.raises(DependencyException, match=r'.*stake amount.*'):
freqtrade._get_trade_stake_amount() freqtrade._get_trade_stake_amount('ETH/BTC')
def test_get_trade_stake_amount_unlimited_amount(default_conf, def test_get_trade_stake_amount_unlimited_amount(default_conf,
@ -224,28 +224,131 @@ def test_get_trade_stake_amount_unlimited_amount(default_conf,
patch_get_signal(freqtrade) patch_get_signal(freqtrade)
# no open trades, order amount should be 'balance / max_open_trades' # no open trades, order amount should be 'balance / max_open_trades'
result = freqtrade._get_trade_stake_amount() result = freqtrade._get_trade_stake_amount('ETH/BTC')
assert result == default_conf['stake_amount'] / conf['max_open_trades'] assert result == default_conf['stake_amount'] / conf['max_open_trades']
# create one trade, order amount should be 'balance / (max_open_trades - num_open_trades)' # create one trade, order amount should be 'balance / (max_open_trades - num_open_trades)'
freqtrade.create_trade() freqtrade.create_trade()
result = freqtrade._get_trade_stake_amount() result = freqtrade._get_trade_stake_amount('LTC/BTC')
assert result == default_conf['stake_amount'] / (conf['max_open_trades'] - 1) assert result == default_conf['stake_amount'] / (conf['max_open_trades'] - 1)
# create 2 trades, order amount should be None # create 2 trades, order amount should be None
freqtrade.create_trade() freqtrade.create_trade()
result = freqtrade._get_trade_stake_amount() result = freqtrade._get_trade_stake_amount('XRP/BTC')
assert result is None assert result is None
# set max_open_trades = None, so do not trade # set max_open_trades = None, so do not trade
conf['max_open_trades'] = 0 conf['max_open_trades'] = 0
freqtrade = FreqtradeBot(conf) freqtrade = FreqtradeBot(conf)
result = freqtrade._get_trade_stake_amount() result = freqtrade._get_trade_stake_amount('NEO/BTC')
assert result is None assert result is None
def test_edge_called_in_process(mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_edge(mocker)
def _refresh_whitelist(list):
return ['ETH/BTC', 'LTC/BTC', 'XRP/BTC', 'NEO/BTC']
patch_exchange(mocker)
freqtrade = FreqtradeBot(edge_conf)
freqtrade._refresh_whitelist = _refresh_whitelist
patch_get_signal(freqtrade)
freqtrade._process()
assert freqtrade.active_pair_whitelist == ['NEO/BTC', 'LTC/BTC']
def test_edge_overrides_stake_amount(mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
freqtrade = FreqtradeBot(edge_conf)
assert freqtrade._get_trade_stake_amount('NEO/BTC') == (0.001 * 0.01) / 0.20
assert freqtrade._get_trade_stake_amount('LTC/BTC') == (0.001 * 0.01) / 0.20
def test_edge_overrides_stoploss(limit_buy_order, fee, markets, caplog, mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
# Strategy stoploss is -0.1 but Edge imposes a stoploss at -0.2
# Thus, if price falls 21%, stoploss should be triggered
#
# mocking the ticker: price is falling ...
buy_price = limit_buy_order['price']
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': buy_price * 0.79,
'ask': buy_price * 0.79,
'last': buy_price * 0.79
}),
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_fee=fee,
get_markets=markets,
)
#############################################
# Create a trade with "limit_buy_order" price
freqtrade = FreqtradeBot(edge_conf)
freqtrade.active_pair_whitelist = ['NEO/BTC']
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.create_trade()
trade = Trade.query.first()
trade.update(limit_buy_order)
#############################################
# stoploss shoud be hit
assert freqtrade.handle_trade(trade) is True
assert log_has('executed sell, reason: SellType.STOP_LOSS', caplog.record_tuples)
assert trade.sell_reason == SellType.STOP_LOSS.value
def test_edge_should_ignore_strategy_stoploss(limit_buy_order, fee, markets,
mocker, edge_conf) -> None:
patch_RPCManager(mocker)
patch_exchange(mocker)
patch_edge(mocker)
# Strategy stoploss is -0.1 but Edge imposes a stoploss at -0.2
# Thus, if price falls 15%, stoploss should not be triggered
#
# mocking the ticker: price is falling ...
buy_price = limit_buy_order['price']
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_ticker=MagicMock(return_value={
'bid': buy_price * 0.85,
'ask': buy_price * 0.85,
'last': buy_price * 0.85
}),
buy=MagicMock(return_value={'id': limit_buy_order['id']}),
get_fee=fee,
get_markets=markets,
)
#############################################
# Create a trade with "limit_buy_order" price
freqtrade = FreqtradeBot(edge_conf)
freqtrade.active_pair_whitelist = ['NEO/BTC']
patch_get_signal(freqtrade)
freqtrade.strategy.min_roi_reached = lambda trade, current_profit, current_time: False
freqtrade.create_trade()
trade = Trade.query.first()
trade.update(limit_buy_order)
#############################################
# stoploss shoud not be hit
assert freqtrade.handle_trade(trade) is False
def test_get_min_pair_stake_amount(mocker, default_conf) -> None: def test_get_min_pair_stake_amount(mocker, default_conf) -> None:
patch_RPCManager(mocker) patch_RPCManager(mocker)
patch_exchange(mocker) patch_exchange(mocker)
@ -494,7 +597,7 @@ def test_create_trade_limit_reached(default_conf, ticker, limit_buy_order,
patch_get_signal(freqtrade) patch_get_signal(freqtrade)
assert freqtrade.create_trade() is False assert freqtrade.create_trade() is False
assert freqtrade._get_trade_stake_amount() is None assert freqtrade._get_trade_stake_amount('ETH/BTC') is None
def test_create_trade_no_pairs(default_conf, ticker, limit_buy_order, fee, markets, mocker) -> None: def test_create_trade_no_pairs(default_conf, ticker, limit_buy_order, fee, markets, mocker) -> None:
@ -593,7 +696,7 @@ def test_process_trade_creation(default_conf, ticker, limit_buy_order,
assert trade.amount == 90.99181073703367 assert trade.amount == 90.99181073703367
assert log_has( assert log_has(
'Checking buy signals to create a new trade with stake_amount: 0.001000 ...', 'Buy signal found: about create a new trade with stake_amount: 0.001000 ...',
caplog.record_tuples caplog.record_tuples
) )
@ -1547,7 +1650,7 @@ def test_sell_profit_only_enable_loss(default_conf, limit_buy_order, fee, market
freqtrade = FreqtradeBot(default_conf) freqtrade = FreqtradeBot(default_conf)
patch_get_signal(freqtrade) patch_get_signal(freqtrade)
freqtrade.strategy.stop_loss_reached = \ freqtrade.strategy.stop_loss_reached = \
lambda current_rate, trade, current_time, current_profit: SellCheckTuple( lambda current_rate, trade, current_time, force_stoploss, current_profit: SellCheckTuple(
sell_flag=False, sell_type=SellType.NONE) sell_flag=False, sell_type=SellType.NONE)
freqtrade.create_trade() freqtrade.create_trade()

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@ -24,3 +24,9 @@ scikit-optimize==0.5.2
# Required for plotting data # Required for plotting data
#plotly==3.1.1 #plotly==3.1.1
# find first, C search in arrays
py_find_1st==1.1.2
#Load ticker files 30% faster
ujson==1.35

View File

@ -37,6 +37,8 @@ setup(name='freqtrade',
'cachetools', 'cachetools',
'coinmarketcap', 'coinmarketcap',
'scikit-optimize', 'scikit-optimize',
'ujson',
'py_find_1st'
], ],
include_package_data=True, include_package_data=True,
zip_safe=False, zip_safe=False,