Feat/add notebooks

quants_lab/controllers/supertrend.py

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+import time
+
+import pandas as pd
+from pydantic import Field
+
+from hummingbot.smart_components.executors.position_executor.position_executor import PositionExecutor
+from hummingbot.smart_components.strategy_frameworks.data_types import OrderLevel
+from hummingbot.smart_components.strategy_frameworks.directional_trading.directional_trading_controller_base import (
+    DirectionalTradingControllerBase,
+    DirectionalTradingControllerConfigBase,
+)
+
+
+class SuperTrendConfig(DirectionalTradingControllerConfigBase):
+    strategy_name: str = "supertrend"
+    length: int = Field(default=20, ge=5, le=200)
+    multiplier: float = Field(default=4.0, ge=2.0, le=7.0)
+    percentage_threshold: float = Field(default=0.01, ge=0.005, le=0.05)
+
+
+class SuperTrend(DirectionalTradingControllerBase):
+    def __init__(self, config: SuperTrendConfig):
+        super().__init__(config)
+        self.config = config
+
+    def early_stop_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
+        # If an executor has an active position, should we close it based on a condition. This feature is not available
+        # for the backtesting yet
+        return False
+
+    def cooldown_condition(self, executor: PositionExecutor, order_level: OrderLevel) -> bool:
+        # After finishing an order, the executor will be in cooldown for a certain amount of time.
+        # This prevents the executor from creating a new order immediately after finishing one and execute a lot
+        # of orders in a short period of time from the same side.
+        if executor.close_timestamp and executor.close_timestamp + order_level.cooldown_time > time.time():
+            return True
+        return False
+
+    def get_processed_data(self) -> pd.DataFrame:
+        df = self.candles[0].candles_df
+        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"]
+
+        # 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)
+
+        # Choose side
+        df['signal'] = 0
+        df.loc[long_condition, 'signal'] = 1
+        df.loc[short_condition, 'signal'] = -1
+        return df

quants_lab/research_notebooks/01_strategy_design_supertrend.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# RESEARCH NOTEBOOK --> SUPERTREND"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import pandas_ta as ta  # noqa: F401\n",
+    "\n",
+    "candles = pd.read_csv(\n",
+    "    \"/Users/dardonacci/Documents/work/dashboard/data/candles/candles_binance_perpetual_WLD-USDT_3m.csv\")"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "candles.head()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "super_trend_lenght = 20\n",
+    "super_trend_multiplier = 3\n",
+    "candles.ta.supertrend(length=super_trend_lenght, multiplier=super_trend_multiplier)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "candles.ta.supertrend(length=super_trend_lenght, multiplier=super_trend_multiplier, append=True)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "candles[\"date\"] = pd.to_datetime(candles[\"timestamp\"], unit='ms')\n",
+    "candles.head()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "\n",
+    "# We are going to use just a subset to see the graph better\n",
+    "candles = candles.tail(800)\n",
+    "\n",
+    "# Create a candlestick chart\n",
+    "fig = go.Figure(data=[go.Candlestick(\n",
+    "    x=candles['date'],\n",
+    "    open=candles['open'],\n",
+    "    high=candles['high'],\n",
+    "    low=candles['low'],\n",
+    "    close=candles['close'])\n",
+    "])\n",
+    "fig.show()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "super_trend_long = candles[candles[\"SUPERTd_20_3.0\"] == 1]\n",
+    "super_trend_short = candles[candles[\"SUPERTd_20_3.0\"] == -1]\n",
+    "# Add the SuperTrend line\n",
+    "fig.add_trace(go.Scatter(x=super_trend_long['date'], y=super_trend_long['SUPERT_20_3.0'],\n",
+    "                         mode='markers',\n",
+    "                         name='SuperTrend Long',\n",
+    "                         line=dict(color=\"green\"),\n",
+    "                         ))\n",
+    "# Add the SuperTrend line\n",
+    "fig.add_trace(go.Scatter(x=super_trend_short['date'], y=super_trend_short['SUPERT_20_3.0'],\n",
+    "                         mode='markers',\n",
+    "                         name='SuperTrend Short',\n",
+    "                         line=dict(color=\"red\")))"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "percentage_threshold = 0.01  # This is an example threshold value\n",
+    "\n",
+    "candles[\"percentage_distance\"] = abs(candles[\"close\"] - candles[\"SUPERT_20_3.0\"]) / candles[\"close\"]\n",
+    "\n",
+    "candles[\"signal\"] = 0\n",
+    "long_condition = (candles[\"SUPERTd_20_3.0\"] == 1) & (candles[\"percentage_distance\"] < percentage_threshold)\n",
+    "short_condition = (candles[\"SUPERTd_20_3.0\"] == -1) & (candles[\"percentage_distance\"] < percentage_threshold)\n",
+    "\n",
+    "candles.loc[long_condition, \"signal\"] = 1\n",
+    "candles.loc[short_condition, \"signal\"] = -1"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from plotly.subplots import make_subplots\n",
+    "\n",
+    "fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.02, subplot_titles=('OHLC', 'Signal'),\n",
+    "                    row_heights=[0.7, 0.3])\n",
+    "\n",
+    "# Add candlestick\n",
+    "fig.add_trace(go.Candlestick(\n",
+    "    x=candles['date'],\n",
+    "    open=candles['open'],\n",
+    "    high=candles['high'],\n",
+    "    low=candles['low'],\n",
+    "    close=candles['close']),\n",
+    "    row=1, col=1)\n",
+    "\n",
+    "# Add the SuperTrend line\n",
+    "fig.add_trace(go.Scatter(x=super_trend_long['date'], y=super_trend_long['SUPERT_20_3.0'],\n",
+    "                         mode='markers',\n",
+    "                         name='SuperTrend Long',\n",
+    "                         line=dict(color=\"green\")),\n",
+    "              row=1, col=1)\n",
+    "# Add the SuperTrend line\n",
+    "fig.add_trace(go.Scatter(x=super_trend_short['date'], y=super_trend_short['SUPERT_20_3.0'],\n",
+    "                         mode='markers',\n",
+    "                         name='SuperTrend Short',\n",
+    "                         line=dict(color=\"red\")),\n",
+    "              row=1, col=1)\n",
+    "\n",
+    "# Add the signal line\n",
+    "fig.add_trace(go.Scatter(x=candles['date'], y=candles['signal'],\n",
+    "                         mode='lines',\n",
+    "                         name='SuperTrend',\n",
+    "                         line=dict(color=\"white\")),\n",
+    "              row=2, col=1)\n",
+    "\n",
+    "# Update x-axis and grid properties\n",
+    "fig.update_xaxes(showline=True, linewidth=2, linecolor='grey', gridcolor='lightgrey')\n",
+    "fig.update_yaxes(showline=True, linewidth=2, linecolor='grey', gridcolor='lightgrey')\n",
+    "\n",
+    "# Update layout to adjust the size and title\n",
+    "fig.update_layout(height=800, title_text=\"OHLC Chart with SuperTrend and Signals\",\n",
+    "                  yaxis_title='Price',\n",
+    "                  xaxis_rangeslider_visible=False)\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}