320 lines
9.2 KiB
Plaintext
320 lines
9.2 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."
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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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"from pathlib import Path\n",
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"from freqtrade.configuration import Configuration\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, use existing configuration file\n",
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"# config = Configuration.from_files([\"config.json\"])\n",
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"\n",
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"# Define some constants\n",
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"config[\"ticker_interval\"] = \"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 = Path(config['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": 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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"\n",
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"candles = load_pair_history(datadir=data_location,\n",
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" timeframe=config[\"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": 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.resolvers import StrategyResolver\n",
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"strategy = StrategyResolver.load_strategy(config)\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=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\n",
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"\n",
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"# Load backtest results\n",
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"trades = load_backtest_data(config[\"user_data_dir\"] / \"backtest_results/backtest-result.json\")\n",
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"\n",
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"# Show value-counts per pair\n",
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"trades.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\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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"# 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\")[\"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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"## 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 `--disable-max-market-positions`.\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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"# 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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"# 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(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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" )\n",
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"\n",
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"\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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"# Show graph inline\n",
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"# graph.show()\n",
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"\n",
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"# Render graph in a seperate window\n",
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"graph.show(renderer=\"browser\")\n"
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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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"name": "python3"
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"pygments_lexer": "ipython3",
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"version": "3.7.4"
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"npconvert_exporter": "python",
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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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"varInspector": {
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"kernels_config": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"varRefreshCmd": "print(var_dic_list())"
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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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