aljaz/hummingbot-dashboard
1
0
Fork 0
mirror of https://github.com/aljazceru/hummingbot-dashboard.git synced 2026-02-20 12:35:42 +01:00
Code Issues Packages Projects Releases Wiki Activity

(feat) refactor directional strategy template

Browse Source
This commit is contained in:
cardosofede
2023-07-21 13:06:57 +02:00
parent c36df3f7e0
commit 67ad71cb54
1 changed files with 22 additions and 32 deletions
Download Patch File Download Diff File Expand all files Collapse all files

54
utils/file_templates.py
View File

@@ -1,60 +1,51 @@
from typing import Dict
def directional_strategy_template(strategy_name: str,
exchange: str,
trading_pair: str,
interval: str) -> str:
def directional_strategy_template(strategy_cls_name: str) -> str:
strategy_config_cls_name = f"{strategy_cls_name}Config"
sma_config_text = "{self.config.sma_length}"
return f"""import pandas_ta as ta
import pandas as pd
import numpy as np
from pydantic import BaseModel, Field
from quants_lab.strategy.directional_strategy_base import DirectionalStrategyBase
class {strategy_config_cls_name}(BaseModel):
exchange: str = Field(default="binance_perpetual")
trading_pair: str = Field(default="ETH-USDT")
interval: str = Field(default="1h")
sma_length: int = Field(default=20, ge=10, le=200)
# ... Add more fields here
class {strategy_name}(DirectionalStrategyBase):
# Define the attributes of the strategy
def __init__(self,
exchange="{exchange}",
trading_pair="{trading_pair}",
interval="{interval}"):
self.exchange = exchange
self.trading_pair = trading_pair
self.interval = interval
class {strategy_cls_name}(DirectionalStrategyBase[{strategy_config_cls_name}]):
def get_raw_data(self):
# The method get candles will search for the data in the folder data/candles
# If the data is not there, you can use the candles downloader to get the data
df = self.get_candles(
exchange=self.exchange,
trading_pair=self.trading_pair,
interval=self.interval,
exchange=self.config.exchange,
trading_pair=self.config.trading_pair,
interval=self.config.interval,
)
return df
def add_indicators(self, df):
df.ta.sma(length=20, append=True)
def preprocessing(self, df):
df.ta.sma(length=self.config.sma_length, append=True)
# ... Add more indicators here
# ... Check https://github.com/twopirllc/pandas-ta#indicators-by-category for more indicators
# ... Use help(ta.indicator_name) to get more info
return df
def add_signals(self, df):
# ... Do your own logic
random_series = pd.Series(np.random.randint(low=0, high=101, size=100))
random_series_2 = pd.Series(np.random.randint(low=0, high=101, size=100))
random_thold = np.random.randint(low=45, high=65)
random_thold_2 = np.random.randint(low=45, high=65)
def predict(self, df):
# Generate long and short conditions
macd_long_cond = (random_series > random_thold) & (random_series_2 > random_thold_2)
macd_short_cond = (random_series < random_thold) & (random_series_2 > random_thold_2)
long_cond = (df['close'] > df[f'SMA_{sma_config_text}'])
short_cond = (df['close'] < df[f'SMA_{sma_config_text}'])
# Choose side
df['side'] = 0
df.loc[macd_long_cond, 'side'] = 1
df.loc[macd_short_cond, 'side'] = -1
df.loc[long_cond, 'side'] = 1
df.loc[short_cond, 'side'] = -1
return df
"""
@@ -122,7 +113,6 @@ def objective(trial):
trial.set_user_attr("avg_trading_time_in_hours", strategy_analysis.avg_trading_time_in_minutes() / 60)
return strategy_analysis.net_profit_pct()
except Exception as e:
# TODO: Log error
traceback.print_exc()
raise TrialPruned()
"""