mirror of
https://github.com/freqtrade/freqtrade.git
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481 lines
14 KiB
Plaintext
481 lines
14 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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"# Strategy analysis 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.\n",
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"The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
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"Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
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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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"## Setup\n",
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"\n",
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"### Change Working directory to repository root"
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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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"import os\n",
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"from pathlib import Path\n",
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"\n",
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"\n",
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"# Change directory\n",
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"# Modify this cell to insure that the output shows the correct path.\n",
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"# Define all paths relative to the project root shown in the cell output\n",
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"project_root = \"somedir/freqtrade\"\n",
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"i = 0\n",
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"try:\n",
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" os.chdir(project_root)\n",
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" if not Path(\"LICENSE\").is_file():\n",
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" i = 0\n",
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" while i < 4 and (not Path(\"LICENSE\").is_file()):\n",
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" os.chdir(Path(Path.cwd(), \"../\"))\n",
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" i += 1\n",
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" project_root = Path.cwd()\n",
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"except FileNotFoundError:\n",
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" print(\"Please define the project root relative to the current directory\")\n",
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"print(Path.cwd())"
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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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"### Configure Freqtrade environment"
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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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"from freqtrade.configuration import Configuration\n",
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"\n",
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"\n",
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"# Customize these according to your needs.\n",
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"\n",
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"# Initialize empty configuration object\n",
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"config = Configuration.from_files([])\n",
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"# Optionally (recommended), use existing configuration file\n",
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"# config = Configuration.from_files([\"user_data/config.json\"])\n",
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"\n",
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"# Define some constants\n",
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"config[\"timeframe\"] = \"5m\"\n",
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"# Name of the strategy class\n",
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"config[\"strategy\"] = \"SampleStrategy\"\n",
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"# Location of the data\n",
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"data_location = config[\"datadir\"]\n",
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"# Pair to analyze - 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": "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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"from freqtrade.data.history import load_pair_history\n",
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"from freqtrade.enums import CandleType\n",
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"\n",
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"\n",
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"candles = load_pair_history(\n",
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" datadir=data_location,\n",
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" timeframe=config[\"timeframe\"],\n",
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" pair=pair,\n",
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" data_format=\"json\", # Make sure to update this to your data\n",
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" candle_type=CandleType.SPOT,\n",
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")\n",
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"\n",
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"# Confirm success\n",
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"print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
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"candles.head()"
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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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"from freqtrade.data.dataprovider import DataProvider\n",
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"from freqtrade.resolvers import StrategyResolver\n",
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"\n",
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"\n",
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"strategy = StrategyResolver.load_strategy(config)\n",
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"strategy.dp = DataProvider(config, None, None)\n",
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"strategy.ft_bot_start()\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(candles, {\"pair\": pair})\n",
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"df.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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"### Display the trade details\n",
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"\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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"* Some possible problems\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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"* Comparison with full backtest\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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" * 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). 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['enter_long'].sum()} entry signals\")\n",
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"data = df.set_index(\"date\", drop=False)\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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"## Load existing objects into a Jupyter notebook\n",
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"\n",
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"The following cells assume that you have already generated data using the cli. \n",
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"They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload."
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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 to pandas dataframe\n",
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"\n",
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"Analyze a trades dataframe (also used below for plotting)"
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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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"from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats\n",
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"\n",
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"\n",
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"# if backtest_dir points to a directory, it'll automatically load the last backtest file.\n",
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"backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
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"# backtest_dir can also point to a specific file\n",
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"# backtest_dir = (\n",
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"# config[\"user_data_dir\"] / \"backtest_results/backtest-result-2020-07-01_20-04-22.json\"\n",
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"# )"
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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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"# You can get the full backtest statistics by using the following command.\n",
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"# This contains all information used to generate the backtest result.\n",
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"stats = load_backtest_stats(backtest_dir)\n",
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"\n",
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"strategy = \"SampleStrategy\"\n",
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"# All statistics are available per strategy, so if `--strategy-list` was used during backtest,\n",
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"# this will be reflected here as well.\n",
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"# Example usages:\n",
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"print(stats[\"strategy\"][strategy][\"results_per_pair\"])\n",
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"# Get pairlist used for this backtest\n",
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"print(stats[\"strategy\"][strategy][\"pairlist\"])\n",
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"# Get market change (average change of all pairs from start to end of the backtest period)\n",
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"print(stats[\"strategy\"][strategy][\"market_change\"])\n",
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"# Maximum drawdown ()\n",
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"print(stats[\"strategy\"][strategy][\"max_drawdown\"])\n",
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"# Maximum drawdown start and end\n",
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"print(stats[\"strategy\"][strategy][\"drawdown_start\"])\n",
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"print(stats[\"strategy\"][strategy][\"drawdown_end\"])\n",
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"\n",
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"\n",
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"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
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"print(stats[\"strategy_comparison\"])"
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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 backtested trades as dataframe\n",
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"trades = load_backtest_data(backtest_dir)\n",
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"\n",
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"# Show value-counts per pair\n",
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"trades.groupby(\"pair\")[\"exit_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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"## Plotting daily profit / equity line"
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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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"# Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)\n",
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"\n",
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"\n",
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"from freqtrade.configuration import Configuration\n",
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"from freqtrade.data.btanalysis import load_backtest_stats\n",
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"\n",
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"\n",
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"# strategy = 'SampleStrategy'\n",
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"# config = Configuration.from_files([\"user_data/config.json\"])\n",
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"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
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"\n",
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"stats = load_backtest_stats(backtest_dir)\n",
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"strategy_stats = stats[\"strategy\"][strategy]\n",
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"\n",
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"df = pd.DataFrame(columns=[\"dates\", \"equity\"], data=strategy_stats[\"daily_profit\"])\n",
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"df[\"equity_daily\"] = df[\"equity\"].cumsum()\n",
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"\n",
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"fig = px.line(df, x=\"dates\", y=\"equity_daily\")\n",
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"fig.show()"
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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\n",
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"\n",
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"In case you did already some trading and want to analyze your performance"
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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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"from freqtrade.data.btanalysis import load_trades_from_db\n",
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"\n",
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"\n",
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"# Fetch trades from database\n",
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"trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
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"\n",
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"# Display results\n",
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"trades.groupby(\"pair\")[\"exit_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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"## Analyze the loaded trades for trade parallelism\n",
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"This can be useful to find the best `max_open_trades` parameter, when used with backtesting in conjunction with a very high `max_open_trades` setting.\n",
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"\n",
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"`analyze_trade_parallelism()` returns a timeseries dataframe with an \"open_trades\" column, specifying the number of open trades for each candle."
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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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"from freqtrade.data.btanalysis import analyze_trade_parallelism\n",
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"\n",
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"\n",
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"# Analyze the above\n",
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"parallel_trades = analyze_trade_parallelism(trades, \"5m\")\n",
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"\n",
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"parallel_trades.plot()"
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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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"## Plot results\n",
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"\n",
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"Freqtrade offers interactive plotting capabilities based on plotly."
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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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"from freqtrade.plot.plotting import generate_candlestick_graph\n",
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"\n",
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"\n",
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"# Limit graph period to keep plotly quick and reactive\n",
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"\n",
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"# Filter trades to one pair\n",
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"trades_red = trades.loc[trades[\"pair\"] == pair]\n",
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"\n",
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"data_red = data[\"2019-06-01\":\"2019-06-10\"]\n",
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"# Generate candlestick graph\n",
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"graph = generate_candlestick_graph(\n",
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" pair=pair,\n",
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" data=data_red,\n",
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" trades=trades_red,\n",
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" indicators1=[\"sma20\", \"ema50\", \"ema55\"],\n",
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" indicators2=[\"rsi\", \"macd\", \"macdsignal\", \"macdhist\"],\n",
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")"
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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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"# Show graph inline\n",
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"# graph.show()\n",
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"\n",
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"# Render graph in a separate window\n",
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"graph.show(renderer=\"browser\")"
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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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"## Plot average profit per trade as distribution graph"
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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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"import plotly.figure_factory as ff\n",
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"\n",
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"\n",
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"hist_data = [trades.profit_ratio]\n",
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"group_labels = [\"profit_ratio\"] # name of the dataset\n",
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"\n",
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"fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n",
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"fig.show()"
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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 3.9.7 64-bit",
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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": "3.11.4"
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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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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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},
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"version": 3,
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|
"vscode": {
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|
"interpreter": {
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"hash": "675f32a300d6d26767470181ad0b11dd4676bcce7ed1dd2ffe2fbc370c95fc7c"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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