adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add documentation for merge_informative_pair helper

Browse Source
This commit is contained in:
Matthias
2020-09-04 20:02:31 +02:00
parent bd4f3d838a
commit 7bc8927914
4 changed files with 71 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files

79
docs/strategy-customization.md
View File

@@ -483,9 +483,8 @@ if self.dp:
### Complete Data-provider sample
```python
from freqtrade.strategy import IStrategy, timeframe_to_minutes
from freqtrade.strategy import IStrategy, merge_informative_pairs
from pandas import DataFrame
import pandas as pd
class SampleStrategy(IStrategy):
# strategy init stuff...
@@ -517,23 +516,12 @@ class SampleStrategy(IStrategy):
# Get the 14 day rsi
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Shift date by 1 candle
# This is necessary since the data is always the "open date"
# and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
minutes = timeframe_to_minutes(inf_tf)
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
# Use the helper function merge_informative_pair to safely merge the pair
# Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
# Full documentation of this method, see below
dataframe = merge_informative_pair(dataframe, informative_pairs, inf_tf, ffill=True)
# Calculate rsi of the original dataframe (5m timeframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@@ -557,6 +545,63 @@ class SampleStrategy(IStrategy):
***
## Helper functions
### *merge_informative_pair()*
This method helps you merge an informative pair to a regular dataframe without lookahead bias.
It's there to help you merge the dataframe in a safe and consistent way.
Options:
- Rename the columns for you to create unique columns
- Merge the dataframe without lookahead bias
- Forward-fill (optional)
All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
!!! Example "Column renaming"
Assuming `inf_tf = '1d'` the resulting columns will be:
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d' # from the informative dataframe
```
??? Example "Column renaming - 1h"
Assuming `inf_tf = '1h'` the resulting columns will be:
``` python
'date', 'open', 'high', 'low', 'close', 'rsi' # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h' # from the informative dataframe
```
??? Example "Custom implementation"
A custom implementation for this is possible, and can be done as follows:
``` python
# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
# Shift date by 1 candle
# This is necessary since the data is always the "open date"
# and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
minutes = timeframe_to_minutes(inf_tf)
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()
```
***
## Additional data (Wallets)
The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.