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improve price df handling to enable backtesting

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This commit is contained in:
robcaulk
2022-08-17 12:51:14 +02:00
parent 2080ff86ed
commit b90da46b1b
8 changed files with 77 additions and 59 deletions
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15
freqtrade/freqai/example_strats/ReinforcementLearningExample3ac.py
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@@ -36,7 +36,7 @@ class ReinforcementLearningExample3ac(IStrategy):
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
can_short = True
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
@@ -76,8 +76,11 @@ class ReinforcementLearningExample3ac(IStrategy):
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
# Raw price currently necessary for RL models:
informative[f"%-{coin}raw_price"] = informative["close"]
# The following features are necessary for RL models
informative[f"%-{coin}raw_close"] = informative["close"]
informative[f"%-{coin}raw_open"] = informative["open"]
informative[f"%-{coin}raw_high"] = informative["high"]
informative[f"%-{coin}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
@@ -101,9 +104,9 @@ class ReinforcementLearningExample3ac(IStrategy):
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
# For RL, this is not a target, it is simply a filler until actions come out
# of the model.
# for Base3ActionEnv, 2 is netural (hold)
df["&-action"] = 2
return df

12
freqtrade/freqai/example_strats/ReinforcementLearningExample5ac.py
View File

@@ -76,8 +76,11 @@ class ReinforcementLearningExample5ac(IStrategy):
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
# Raw price currently necessary for RL models:
informative[f"%-{coin}raw_price"] = informative["close"]
# The following features are necessary for RL models
informative[f"%-{coin}raw_close"] = informative["close"]
informative[f"%-{coin}raw_open"] = informative["open"]
informative[f"%-{coin}raw_high"] = informative["high"]
informative[f"%-{coin}raw_low"] = informative["low"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
@@ -101,9 +104,8 @@ class ReinforcementLearningExample5ac(IStrategy):
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
# For RL, there are no direct targets to set. This is filler (neutral)
# until the agent sends an action.
df["&-action"] = 2
return df