From 7453dac668274f02dbff38a0d6dea69197b3b9f5 Mon Sep 17 00:00:00 2001 From: Matthias Date: Sat, 24 Apr 2021 13:13:41 +0200 Subject: [PATCH] Improve doc wording --- docs/hyperopt.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/hyperopt.md b/docs/hyperopt.md index bea8dc256..b3fdc699b 100644 --- a/docs/hyperopt.md +++ b/docs/hyperopt.md @@ -198,8 +198,7 @@ There you have two different types of indicators: 1. `guards` and 2. `triggers`. However, this guide will make this distinction to make it clear that signals should not be "sticking". Sticking signals are signals that are active for multiple candles. This can lead into buying a signal late (right before the signal disappears - which means that the chance of success is a lot lower than right at the beginning). -Hyper-optimization will, for each epoch round, pick one trigger and possibly -multiple guards. +Hyper-optimization will, for each epoch round, pick one trigger and possibly multiple guards. #### Sell optimization @@ -266,8 +265,6 @@ The last one we call `trigger` and use it to decide which buy trigger we want to So let's write the buy strategy using these values: ```python - - def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: conditions = [] # GUARDS AND TRENDS @@ -327,6 +324,9 @@ There are four parameter types each suited for different purposes. Assuming you have a simple strategy in mind - a EMA cross strategy (2 Moving averages crossing) - and you'd like to find the ideal parameters for this strategy. ``` python +from pandas import DataFrame +from functools import reduce + import talib.abstract as ta from freqtrade.strategy import IStrategy @@ -334,7 +334,7 @@ from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParame import freqtrade.vendor.qtpylib.indicators as qtpylib class MyAwesomeStrategy(IStrategy): - stoploss = 0.5 + stoploss = -0.05 timeframe = '15m' # Define the parameter spaces buy_ema_short = IntParameter(3, 50, default=5)