241 lines
8.7 KiB
Plaintext
241 lines
8.7 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 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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"## Setup"
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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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"# Modify this cell to insure that the output shows the correct path.\n",
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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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"# 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.chdirdir(project_root)\n",
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" assert Path('LICENSE').is_file()\n",
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"except:\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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"print(Path.cwd())"
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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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"# Customize these according to your needs.\n",
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"\n",
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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 = 'TestStrategy'\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 - 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": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'freqtrade'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-b421d7818902>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load data using values set above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpathlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfreqtrade\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_pair_history\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m candles = load_pair_history(datadir=data_location,\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'freqtrade'"
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]
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}
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],
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"source": [
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"# Load data using values set above\n",
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"from pathlib import Path\n",
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"from freqtrade.data.history import load_pair_history\n",
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"\n",
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"candles = load_pair_history(datadir=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(candles)) + f\" 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": 2,
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'freqtrade'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-2-a217d0e0719c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load strategy using values set above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfreqtrade\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolvers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStrategyResolver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m strategy = StrategyResolver({'strategy': strategy_name,\n\u001b[1;32m 4\u001b[0m \u001b[0;34m'user_data_dir'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0muser_data_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m 'strategy_path': strategy_location}).strategy\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'freqtrade'"
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]
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}
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],
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"source": [
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"# Load strategy using values set above\n",
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"from freqtrade.resolvers import StrategyResolver\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(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['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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"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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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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