Feat/tasks improvement

Dockerfile

@@ -14,6 +14,7 @@ COPY core/ core/
 COPY environment.yml .
 COPY research_notebooks/ research_notebooks/
 COPY controllers/ controllers/
+COPY tasks/ tasks/
 
 # Create the environment from the environment.yml file
 # If cchardet fails, we'll install it separately

compose-db.yml

@@ -0,0 +1,28 @@
+services:
+  timescaledb:
+    container_name: timescaledb
+    image: timescale/timescaledb:latest-pg14
+    environment:
+      - POSTGRES_USER=admin
+      - POSTGRES_PASSWORD=admin
+      - POSTGRES_DB=timescaledb
+    volumes:
+      - timescale-data:/var/lib/postgresql/data
+    ports:
+      - "5432:5432"
+  optunadb:
+    container_name: optunadb
+    image: postgres:latest
+    environment:
+      - POSTGRES_USER=admin
+      - POSTGRES_PASSWORD=admin
+      - POSTGRES_DB=optimization_database
+    volumes:
+      - optuna-data:/var/lib/postgresql/data
+    ports:
+      - "5433:5432"
+volumes:
+  timescale-data:
+    driver: local
+  optuna-data:
+    driver: local

compose-tasks.yml

@@ -0,0 +1,54 @@
+services:
+  data-generation-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/data_generation_runner.py
+    network_mode: host
+
+  backtesting-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/backtesting_runner.py
+    network_mode: host
+
+  db-status-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/db_status_runner.py
+    network_mode: host
+
+  candles-downloader-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/candles_downloader_runner.py
+    network_mode: host
+
+  screener-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/screener_runner.py
+    network_mode: host
+
+  report-runner:
+    image: hummingbot/quants-lab:latest
+    volumes:
+      - .:/quants-lab
+    env_file:
+      - .env
+    command: conda run --no-capture-output -n quants-lab python3 tasks/report_runner.py
+    network_mode: host

controllers/directional_trading/smugplug.py

@@ -0,0 +1,109 @@
+from typing import List
+
+import pandas_ta as ta  # noqa: F401
+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 SmugPlugControllerConfig(DirectionalTradingControllerConfigBase):
+    controller_name = "smugplug"
+    candles_config: List[CandlesConfig] = []
+    candles_connector: str = Field(
+        default=None)
+    candles_trading_pair: str = Field(
+        default=None)
+    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))
+    macd_fast: int = Field(
+        default=21,
+        client_data=ClientFieldData(
+            prompt=lambda mi: "Enter the MACD fast period: ",
+            prompt_on_new=True))
+    macd_slow: int = Field(
+        default=42,
+        client_data=ClientFieldData(
+            prompt=lambda mi: "Enter the MACD slow period: ",
+            prompt_on_new=True))
+    macd_signal: int = Field(
+        default=9,
+        client_data=ClientFieldData(
+            prompt=lambda mi: "Enter the MACD signal period: ",
+            prompt_on_new=True))
+    # EMAs
+    ema_short: int = 8
+    ema_medium: int = 29
+    ema_long: int = 31
+
+    # ATR
+    atr_length: int = 11
+    atr_multiplier: float = 1.5
+
+    @validator("candles_connector", pre=True, always=True)
+    def set_candles_connector(cls, v, values):
+        if v is None or v == "":
+            return values.get("connector_name")
+        return v
+
+    @validator("candles_trading_pair", pre=True, always=True)
+    def set_candles_trading_pair(cls, v, values):
+        if v is None or v == "":
+            return values.get("trading_pair")
+        return v
+
+
+class SmugPlugController(DirectionalTradingControllerBase):
+
+    def __init__(self, config: SmugPlugControllerConfig, *args, **kwargs):
+        self.config = config
+        self.max_records = max(config.macd_slow, config.macd_fast, config.macd_signal,
+                               config.atr_length, config.ema_short, config.ema_medium, config.ema_long) + 20
+        if len(self.config.candles_config) == 0:
+            self.config.candles_config = [CandlesConfig(
+                connector=config.candles_connector,
+                trading_pair=config.candles_trading_pair,
+                interval=config.interval,
+                max_records=self.max_records
+            )]
+        super().__init__(config, *args, **kwargs)
+
+    async def update_processed_data(self):
+        df = self.market_data_provider.get_candles_df(connector_name=self.config.candles_connector,
+                                                      trading_pair=self.config.candles_trading_pair,
+                                                      interval=self.config.interval,
+                                                      max_records=self.max_records)
+        # Add indicators
+        df.ta.macd(fast=self.config.macd_fast, slow=self.config.macd_slow, signal=self.config.macd_signal, append=True)
+        df.ta.atr(length=self.config.atr_length, append=True)
+        df.ta.ema(length=self.config.ema_short, append=True)
+        df.ta.ema(length=self.config.ema_medium, append=True)
+        df.ta.ema(length=self.config.ema_long, append=True)
+        df["long_atr_support"] = df["close"].shift(1) - df[
+            f"ATRr_{self.config.atr_length}"] * self.config.atr_multiplier
+        df["short_atr_resistance"] = df["close"].shift(1) + df[
+            f"ATRr_{self.config.atr_length}"] * self.config.atr_multiplier
+
+        macdh = df[f"MACDh_{self.config.macd_fast}_{self.config.macd_slow}_{self.config.macd_signal}"]
+        short_ema = df[f"EMA_{self.config.ema_short}"]
+        medium_ema = df[f"EMA_{self.config.ema_medium}"]
+        long_ema = df[f"EMA_{self.config.ema_long}"]
+        close = df["close"]
+        long_condition = (short_ema > medium_ema) & (medium_ema > long_ema) & (close > short_ema) & \
+                         (close > df["long_atr_support"]) & (macdh > 0)
+        short_condition = (short_ema < medium_ema) & (medium_ema < long_ema) & (close < short_ema) & \
+                          (close < df["short_atr_resistance"]) & (macdh < 0)
+
+        df["signal"] = 0
+        df.loc[long_condition, "signal"] = 1
+        df.loc[short_condition, "signal"] = -1
+
+        # Update processed data
+        self.processed_data["signal"] = df["signal"].iloc[-1]
+        self.processed_data["features"] = df