Merge pull request #24 from hummingbot/feat/clean_up_repo

Feat/clean up repo

.pre-commit-config.yaml

@@ -0,0 +1,33 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    - id: detect-private-key
+
+-   repo: https://github.com/pre-commit/mirrors-eslint
+    rev: v8.10.0
+    hooks:
+    -   id: eslint
+        files: \.[jt]sx?$  # *.js, *.jsx, *.ts and *.tsx
+        types: [file]
+
+-   repo: https://github.com/CoinAlpha/git-hooks
+    rev: 78f0683233a09c68a072fd52740d32c0376d4f0f
+    hooks:
+    -   id: detect-wallet-private-key
+        types: [file]
+        exclude: .json
+
+-   repo: https://github.com/pycqa/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        files: "\\.(py)$"
+        args: [--settings-path=pyproject.toml]
+
+-   repo: https://github.com/pycqa/flake8
+    rev: 3.9.2
+    hooks:
+      - id: flake8
+        additional_dependencies: ['flake8']
+        args: [--max-line-length=130]

bots/controllers/directional_trading/bollinger_v1.py

@@ -1,31 +1,20 @@
 from typing import List
 
 import pandas_ta as ta  # noqa: F401
-from pydantic import Field, validator
-
 from hummingbot.client.config.config_data_types import ClientFieldData
 from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
 from hummingbot.strategy_v2.controllers.directional_trading_controller_base import (
     DirectionalTradingControllerBase,
     DirectionalTradingControllerConfigBase,
 )
+from pydantic import Field, validator
 
 
 class BollingerV1ControllerConfig(DirectionalTradingControllerConfigBase):
     controller_name = "bollinger_v1"
     candles_config: List[CandlesConfig] = []
-    candles_connector: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ", )
-    )
-    candles_trading_pair: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ", )
-    )
+    candles_connector: str = Field(default=None)
+    candles_trading_pair: str = Field(default=None)
     interval: str = Field(
         default="3m",
         client_data=ClientFieldData(

bots/controllers/directional_trading/dman_v3.py

@@ -3,8 +3,6 @@
 from typing import List, Optional, Tuple
 
 import pandas_ta as ta  # noqa: F401
-from pydantic import Field, validator
-
 from hummingbot.client.config.config_data_types import ClientFieldData
 from hummingbot.core.data_type.common import TradeType
 from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
@@ -14,23 +12,14 @@
 )
 from hummingbot.strategy_v2.executors.dca_executor.data_types import DCAExecutorConfig, DCAMode
 from hummingbot.strategy_v2.executors.position_executor.data_types import TrailingStop
+from pydantic import Field, validator
 
 
 class DManV3ControllerConfig(DirectionalTradingControllerConfigBase):
     controller_name: str = "dman_v3"
     candles_config: List[CandlesConfig] = []
-    candles_connector: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ",)
-    )
-    candles_trading_pair: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ",)
-    )
+    candles_connector: str = Field(default=None)
+    candles_trading_pair: str = Field(default=None)
     interval: str = Field(
         default="30m",
         client_data=ClientFieldData(
@@ -61,9 +50,10 @@ class DManV3ControllerConfig(DirectionalTradingControllerConfigBase):
     dca_spreads: List[Decimal] = Field(
         default="0.001,0.018,0.15,0.25",
         client_data=ClientFieldData(
-            prompt=lambda mi: "Enter the spreads for each DCA level (comma-separated) if dynamic_spread=True this value "
-                              "will multiply the Bollinger Bands width, e.g. if the Bollinger Bands width is 0.1 (10%)"
-                              "and the spread is 0.2, the distance of the order to the current price will be 0.02 (2%) ",
+            prompt=lambda
+                mi: "Enter the spreads for each DCA level (comma-separated) if dynamic_spread=True this value "
+                    "will multiply the Bollinger Bands width, e.g. if the Bollinger Bands width is 0.1 (10%)"
+                    "and the spread is 0.2, the distance of the order to the current price will be 0.02 (2%) ",
             prompt_on_new=True))
     dca_amounts_pct: List[Decimal] = Field(
         default=None,
@@ -119,7 +109,9 @@ def validate_amounts(cls, v, values):
             return [Decimal('1.0') / len(spreads) for _ in spreads]
         return v
 
-    def get_spreads_and_amounts_in_quote(self, trade_type: TradeType, total_amount_quote: Decimal) -> Tuple[List[Decimal], List[Decimal]]:
+    def get_spreads_and_amounts_in_quote(self,
+                                         trade_type: TradeType,
+                                         total_amount_quote: Decimal) -> Tuple[List[Decimal], List[Decimal]]:
         amounts_pct = self.dca_amounts_pct
         if amounts_pct is None:
             # Equally distribute if amounts_pct is not set
@@ -151,6 +143,7 @@ class DManV3Controller(DirectionalTradingControllerBase):
     Mean reversion strategy with Grid execution making use of Bollinger Bands indicator to make spreads dynamic
     and shift the mid-price.
     """
+
     def __init__(self, config: DManV3ControllerConfig, *args, **kwargs):
         self.config = config
         self.max_records = config.bb_length
@@ -201,8 +194,9 @@ def get_executor_config(self, trade_type: TradeType, price: Decimal, amount: Dec
             prices = [price * (1 + spread * spread_multiplier) for spread in spread]
         if self.config.dynamic_target:
             stop_loss = self.config.stop_loss * spread_multiplier
-            trailing_stop = TrailingStop(activation_price=self.config.trailing_stop.activation_price * spread_multiplier,
-                                         trailing_delta=self.config.trailing_stop.trailing_delta * spread_multiplier)
+            trailing_stop = TrailingStop(
+                activation_price=self.config.trailing_stop.activation_price * spread_multiplier,
+                trailing_delta=self.config.trailing_stop.trailing_delta * spread_multiplier)
         else:
             stop_loss = self.config.stop_loss
             trailing_stop = self.config.trailing_stop

bots/controllers/directional_trading/macd_bb_v1.py

@@ -1,31 +1,20 @@
 from typing import List
 
 import pandas_ta as ta  # noqa: F401
-from pydantic import Field, validator
-
 from hummingbot.client.config.config_data_types import ClientFieldData
 from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
 from hummingbot.strategy_v2.controllers.directional_trading_controller_base import (
     DirectionalTradingControllerBase,
     DirectionalTradingControllerConfigBase,
 )
+from pydantic import Field, validator
 
 
 class MACDBBV1ControllerConfig(DirectionalTradingControllerConfigBase):
     controller_name = "macd_bb_v1"
     candles_config: List[CandlesConfig] = []
-    candles_connector: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ", )
-    )
-    candles_trading_pair: str = Field(
-        default=None,
-        client_data=ClientFieldData(
-            prompt_on_new=True,
-            prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ", )
-    )
+    candles_connector: str = Field(default=None)
+    candles_trading_pair: str = Field(default=None)
     interval: str = Field(
         default="3m",
         client_data=ClientFieldData(

bots/controllers/directional_trading/supertrend_v1.py

@@ -1,25 +1,28 @@
 from typing import List, Optional
 
 import pandas_ta as ta  # noqa: F401
-from pydantic import Field, validator
-
 from hummingbot.client.config.config_data_types import ClientFieldData
 from hummingbot.data_feed.candles_feed.data_types import CandlesConfig
 from hummingbot.strategy_v2.controllers.directional_trading_controller_base import (
     DirectionalTradingControllerBase,
     DirectionalTradingControllerConfigBase,
 )
+from pydantic import Field, validator
 
 
 class SuperTrendConfig(DirectionalTradingControllerConfigBase):
     controller_name: str = "supertrend_v1"
     candles_config: List[CandlesConfig] = []
-    candles_connector: Optional[str] = Field(default=None, client_data=ClientFieldData(prompt_on_new=True, prompt=lambda mi: "Enter the connector for the candles data, leave empty to use the same exchange as the connector: ", ))
-    candles_trading_pair: Optional[str] = Field(default=None, client_data=ClientFieldData(prompt_on_new=True, prompt=lambda mi: "Enter the trading pair for the candles data, leave empty to use the same trading pair as the connector: ", ))
-    interval: str = Field(default="3m", client_data=ClientFieldData(prompt=lambda mi: "Enter the candle interval (e.g., 1m, 5m, 1h, 1d): ", prompt_on_new=False))
-    length: int = Field(default=20, client_data=ClientFieldData(prompt=lambda mi: "Enter the supertrend length: ", prompt_on_new=True))
-    multiplier: float = Field(default=4.0, client_data=ClientFieldData(prompt=lambda mi: "Enter the supertrend multiplier: ", prompt_on_new=True))
-    percentage_threshold: float = Field(default=0.01, client_data=ClientFieldData(prompt=lambda mi: "Enter the percentage threshold: ", prompt_on_new=True))
+    candles_connector: Optional[str] = Field(default=None)
+    candles_trading_pair: Optional[str] = Field(default=None)
+    interval: str = Field(default="3m")
+    length: int = Field(default=20, client_data=ClientFieldData(prompt=lambda mi: "Enter the supertrend length: ",
+                                                                prompt_on_new=True))
+    multiplier: float = Field(default=4.0,
+                              client_data=ClientFieldData(prompt=lambda mi: "Enter the supertrend multiplier: ",
+                                                          prompt_on_new=True))
+    percentage_threshold: float = Field(default=0.01, client_data=ClientFieldData(
+        prompt=lambda mi: "Enter the percentage threshold: ", prompt_on_new=True))
 
     @validator("candles_connector", pre=True, always=True)
     def set_candles_connector(cls, v, values):
@@ -54,11 +57,14 @@ async def update_processed_data(self):
                                                       max_records=self.max_records)
         # Add indicators
         df.ta.supertrend(length=self.config.length, multiplier=self.config.multiplier, append=True)
-        df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{self.config.length}_{self.config.multiplier}"]) / df["close"]
+        df["percentage_distance"] = abs(df["close"] - df[f"SUPERT_{self.config.length}_{self.config.multiplier}"]) / df[
+            "close"]
 
         # Generate long and short conditions
-        long_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == 1) & (df["percentage_distance"] < self.config.percentage_threshold)
-        short_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == -1) & (df["percentage_distance"] < self.config.percentage_threshold)
+        long_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == 1) & (
+                df["percentage_distance"] < self.config.percentage_threshold)
+        short_condition = (df[f"SUPERTd_{self.config.length}_{self.config.multiplier}"] == -1) & (
+                df["percentage_distance"] < self.config.percentage_threshold)
 
         # Choose side
         df['signal'] = 0