mirror of
https://github.com/aljazceru/hummingbot-dashboard.git
synced 2026-01-07 23:44:24 +01:00
(feat) move analize optimizations to root
This commit is contained in:
cardosofede
10911
quants_lab/research_notebooks/01_analyze_optimization_results.ipynb
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10911
quants_lab/research_notebooks/01_analyze_optimization_results.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"\n",
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"root_path = os.path.abspath(os.path.join(os.getcwd(), '../../..'))\n",
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"sys.path.append(root_path)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"from utils.optuna_database_manager import OptunaDBManager\n",
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"\n",
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"db_root_path = \"../../../data/backtesting/\"\n",
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"db_name = \"backtesting_report.db\"\n",
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"\n",
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"optuna_db_manager = OptunaDBManager(db_name=db_name,\n",
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" db_root_path=db_root_path)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"optuna_db_manager.studies"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"study_name = \"super_trend_optimization_1\"\n",
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"df = optuna_db_manager.merged_df[optuna_db_manager.merged_df[\"study_name\"] == study_name]"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"df"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Bar Chart of Average Trading Time\n",
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"This bar chart compares the average trading time across trials. It helps to quickly identify trials with unusually long or short trading times.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import plotly.express as px\n",
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"\n",
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"fig = px.bar(df, x='trial_id', y='avg_trading_time_in_hours', title='Average Trading Time per Trial')\n",
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"fig.show()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Scatter Plot of Accuracy vs. Average Trading Time\n",
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"This scatter plot shows the relationship between the accuracy and the average trading time of each trial. It can help to identify if there is any correlation between these two metrics.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"fig = px.scatter(df, x='avg_trading_time_in_hours', y='accuracy', title='Accuracy vs. Average Trading Time')\n",
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"fig.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Histogram of Total Positions\n",
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"The histogram represents the distribution of total positions across all trials. This visualization is useful for understanding the general spread and most common values of positions.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"fig = px.histogram(df, x='total_positions', title='Distribution of Total Positions')\n",
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"fig.show()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Pie Chart of Win Signals\n",
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"This pie chart shows the proportion of win signals in each trial, providing a visual representation of the success rate distribution across trials.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"fig = px.pie(df, names='trial_id', values='win_signals', title='Proportion of Win Signals per Trial')\n",
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"fig.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Box Plot for Trial Value\n",
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"A box plot for trial values to identify the range, median, and any potential outliers in the trial values.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"fig = px.box(df, y='value', title='Box Plot of Trial PNL')\n",
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"fig.show()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Heatmap for Correlation Analysis\n",
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"This heatmap illustrates the correlation between various numerical variables such as accuracy, average trading time, total positions, win signals, and value.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import plotly.express as px\n",
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"\n",
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"# Calculate the correlation matrix\n",
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"correlation_matrix = df[['accuracy', 'avg_trading_time_in_hours', 'total_positions', 'win_signals', 'value']].corr()\n",
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"\n",
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"# Generate the heatmap\n",
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"fig = px.imshow(correlation_matrix,\n",
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" x=correlation_matrix.columns,\n",
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" y=correlation_matrix.columns,\n",
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" title='Correlation Heatmap',\n",
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" labels=dict(x=\"Variable\", y=\"Variable\", color=\"Correlation\"),\n",
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" color_continuous_scale='RdBu')\n",
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"\n",
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"fig.show()\n",
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"\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Stacked Bar Chart for Win Signals vs Total Positions\n",
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"A stacked bar chart displaying the ratio of win signals to total positions for each trial. This helps in visualizing the efficiency and effectiveness of each trial.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"df['loss_signals'] = df['total_positions'] - df['win_signals']\n",
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"fig = px.bar(df, x='trial_id', y=['win_signals', 'loss_signals'], title='Win vs Loss Signals per Trial', labels={'value':'Number of Signals'}, hover_data=['total_positions'])\n",
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"fig.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Scatter Plots Against PNL\n",
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"These scatter plots show how various metrics behave in relation to the PNL (Profit and Loss). This analysis can help in understanding which factors have a stronger relationship with financial outcomes.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Scatter Plot for Accuracy vs PNL\n",
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"fig_accuracy_pnl = px.scatter(df, x='accuracy', y='value', title='Accuracy vs PNL')\n",
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"fig_accuracy_pnl.show()\n",
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"\n",
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"# Scatter Plot for Average Trading Time vs PNL\n",
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"fig_tradingtime_pnl = px.scatter(df, x='avg_trading_time_in_hours', y='value', title='Average Trading Time vs PNL')\n",
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"fig_tradingtime_pnl.show()\n",
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"\n",
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"# Scatter Plot for Total Positions vs PNL\n",
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"fig_positions_pnl = px.scatter(df, x='total_positions', y='value', title='Total Positions vs PNL')\n",
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"fig_positions_pnl.show()\n",
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"\n",
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"# Scatter Plot for Win Signals vs PNL\n",
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"fig_winsignals_pnl = px.scatter(df, x='win_signals', y='value', title='Win Signals vs PNL')\n",
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"fig_winsignals_pnl.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Scatter Matrix of All Variables\n",
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"A scatter matrix allows us to see both the distribution of single variables and the relationships between two variables. The diagonal shows histograms for each variable, and the scatter plots show correlations between them.\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import plotly.express as px\n",
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"\n",
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"# Selecting columns for scatter matrix\n",
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"selected_columns = ['accuracy', 'avg_trading_time_in_hours', 'total_positions', 'win_signals', 'value']\n",
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"\n",
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"# Creating scatter matrix\n",
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"fig_matrix = px.scatter_matrix(df[selected_columns], dimensions=selected_columns, title='Scatter Matrix of Variables', height=800)\n",
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"fig_matrix.show()\n"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import json\n",
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"from hummingbot.core.data_type.common import PositionMode\n",
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"from hummingbot.connector.connector_base import TradeType, OrderType\n",
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"from hummingbot.smart_components.utils.config_encoder_decoder import ConfigEncoderDecoder\n",
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"\n",
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"trial_to_analyze = 36\n",
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"\n",
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"trial = df[df[\"trial_id\"] == trial_to_analyze]"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"trial"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"# Transform trial config in a dictionary\n",
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"encoder_decoder = ConfigEncoderDecoder(TradeType, OrderType, PositionMode)\n",
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"trial_config = encoder_decoder.decode(json.loads(trial[\"config\"].item()))\n",
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"trial_config"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"from quants_lab.controllers.supertrend import SuperTrend, SuperTrendConfig\n",
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"\n",
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"# In this case we are using the supertrend controller but we can also access to the controller by using the method load_controllers\n",
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"config = SuperTrendConfig(**trial_config)\n",
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"controller = SuperTrend(config)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"from quants_lab.strategy.strategy_analysis import StrategyAnalysis\n",
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"from hummingbot.smart_components.strategy_frameworks.directional_trading import DirectionalTradingBacktestingEngine\n",
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"\n",
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"# Backtest configuration\n",
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"trade_cost = 0.0006\n",
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"initial_portfolio_usd = 1000\n",
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"start = \"2023-01-01\"\n",
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"end = \"2024-01-02\"\n",
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"\n",
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"# Load the data\n",
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"engine = DirectionalTradingBacktestingEngine(controller=controller)\n",
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"engine.load_controller_data(\"../../data/candles\")\n",
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"backtesting_results = engine.run_backtesting(initial_portfolio_usd=initial_portfolio_usd,\n",
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" trade_cost=trade_cost,\n",
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" start=start, end=end)\n",
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"strategy_analysis = StrategyAnalysis(\n",
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" positions=backtesting_results[\"executors_df\"],\n",
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" candles_df=backtesting_results[\"processed_data\"],\n",
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")"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"strategy_analysis.create_base_figure(volume=False, positions=False, trade_pnl=True)\n",
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"strategy_analysis.figure()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"strategy_analysis.pnl_over_time()"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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