239 lines
6.4 KiB
Plaintext
239 lines
6.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Analyzing bot data\n",
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"\n",
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"You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation)."
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"from pathlib import Path\n",
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"import os\n",
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"from freqtrade.data.history import load_pair_history\n",
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"from freqtrade.resolvers import StrategyResolver\n",
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"from freqtrade.data.btanalysis import load_backtest_data\n",
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"from freqtrade.data.btanalysis import load_trades_from_db"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Change directory\n",
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"# Define all paths relative to the project root shown in the cell output\n",
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"try:\n",
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" os.chdir(Path(Path.cwd(), '../..'))\n",
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" print(Path.cwd())\n",
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"except:\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example snippets"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load backtest results into a pandas dataframe"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load backtest results\n",
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"df = load_backtest_data(\"user_data/backtest_data/backtest-result.json\")\n",
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"\n",
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"# Show value-counts per pair\n",
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"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load live trading results into a pandas dataframe"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Fetch trades from database\n",
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"df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
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"\n",
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"# Display results\n",
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"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Strategy debugging example\n",
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"\n",
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"Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Import requirements and define variables used in analyses"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Define some constants\n",
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"ticker_interval = \"5m\"\n",
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"# Name of the strategy class\n",
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"strategy_name = 'NewStrategy'\n",
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"# Path to user data\n",
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"user_data_dir = 'user_data'\n",
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"# Location of the strategy\n",
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"strategy_location = Path(user_data_dir, 'strategies')\n",
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"# Location of the data\n",
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"data_location = Path(user_data_dir, 'data', 'binance')\n",
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"# Pair to analyze \n",
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"# Only use one pair here\n",
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"pair = \"BTC_USDT\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load exchange data"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load data using values set above\n",
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"bt_data = load_pair_history(datadir=Path(data_location),\n",
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" ticker_interval=ticker_interval,\n",
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" pair=pair)\n",
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"\n",
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"# Confirm success\n",
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"print(\"Loaded \" + str(len(bt_data)) + f\" rows of data for {pair} from {data_location}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Load and run strategy\n",
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"* Rerun each time the strategy file is changed"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Load strategy using values set above\n",
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"strategy = StrategyResolver({'strategy': strategy_name,\n",
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" 'user_data_dir': user_data_dir,\n",
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" 'strategy_path': strategy_location}).strategy\n",
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"\n",
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"# Generate buy/sell signals using strategy\n",
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"df = strategy.analyze_ticker(bt_data, {'pair': pair})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Display the trade details\n",
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"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
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"\n",
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"#### Some possible problems\n",
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"\n",
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"* Columns with NaN values at the end of the dataframe\n",
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"* Columns used in `crossed*()` functions with completely different units\n",
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"\n",
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"#### Comparison with full backtest\n",
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"\n",
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"having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
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"\n",
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"Assuming you use only one condition such as, `df['rsi'] < 30` as buy condition, this will generate multiple \"buy\" signals for each pair in sequence (until rsi returns > 29).\n",
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"The bot will only buy on the first of these signals (and also only if a trade-slot (\"max_open_trades\") is still available), or on one of the middle signals, as soon as a \"slot\" becomes available.\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Report results\n",
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"print(f\"Generated {df['buy'].sum()} buy signals\")\n",
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"data = df.set_index('date', drop=True)\n",
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"data.tail()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data."
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]
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}
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],
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"metadata": {
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"file_extension": ".py",
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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": 3
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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": "ipython3",
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"version": "3.7.3"
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},
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"mimetype": "text/x-python",
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"name": "python",
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"npconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": 3
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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