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<h1 id="strategy-analysis-example">Strategy analysis example<a class="headerlink" href="#strategy-analysis-example" title="Permanent link">&para;</a></h1>
<p>Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
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.</p>
<h2 id="setup">Setup<a class="headerlink" href="#setup" title="Permanent link">&para;</a></h2>
<p>```python
from pathlib import Path
from freqtrade.configuration import Configuration</p>
<h1 id="customize-these-according-to-your-needs">Customize these according to your needs.<a class="headerlink" href="#customize-these-according-to-your-needs" title="Permanent link">&para;</a></h1>
<h1 id="initialize-empty-configuration-object">Initialize empty configuration object<a class="headerlink" href="#initialize-empty-configuration-object" title="Permanent link">&para;</a></h1>
<p>config = Configuration.from_files([])</p>
<h1 id="optionally-use-existing-configuration-file">Optionally, use existing configuration file<a class="headerlink" href="#optionally-use-existing-configuration-file" title="Permanent link">&para;</a></h1>
<h1 id="config-configurationfrom_filesconfigjson">config = Configuration.from_files(["config.json"])<a class="headerlink" href="#config-configurationfrom_filesconfigjson" title="Permanent link">&para;</a></h1>
<h1 id="define-some-constants">Define some constants<a class="headerlink" href="#define-some-constants" title="Permanent link">&para;</a></h1>
<p>config["timeframe"] = "5m"</p>
<h1 id="name-of-the-strategy-class">Name of the strategy class<a class="headerlink" href="#name-of-the-strategy-class" title="Permanent link">&para;</a></h1>
<p>config["strategy"] = "SampleStrategy"</p>
<h1 id="location-of-the-data">Location of the data<a class="headerlink" href="#location-of-the-data" title="Permanent link">&para;</a></h1>
<p>data_location = Path(config['user_data_dir'], 'data', 'binance')</p>
<h1 id="pair-to-analyze-only-use-one-pair-here">Pair to analyze - Only use one pair here<a class="headerlink" href="#pair-to-analyze-only-use-one-pair-here" title="Permanent link">&para;</a></h1>
<p>pair = "BTC/USDT"
```</p>
<p>```python</p>
<h1 id="load-data-using-values-set-above">Load data using values set above<a class="headerlink" href="#load-data-using-values-set-above" title="Permanent link">&para;</a></h1>
<p>from freqtrade.data.history import load_pair_history</p>
<p>candles = load_pair_history(datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "hdf5",
)</p>
<h1 id="confirm-success">Confirm success<a class="headerlink" href="#confirm-success" title="Permanent link">&para;</a></h1>
<p>print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
candles.head()
```</p>
<h2 id="load-and-run-strategy">Load and run strategy<a class="headerlink" href="#load-and-run-strategy" title="Permanent link">&para;</a></h2>
<ul>
<li>Rerun each time the strategy file is changed</li>
</ul>
<p>```python</p>
<h1 id="load-strategy-using-values-set-above">Load strategy using values set above<a class="headerlink" href="#load-strategy-using-values-set-above" title="Permanent link">&para;</a></h1>
<p>from freqtrade.resolvers import StrategyResolver
from freqtrade.data.dataprovider import DataProvider
strategy = StrategyResolver.load_strategy(config)
strategy.dp = DataProvider(config, None, None)</p>
<h1 id="generate-buysell-signals-using-strategy">Generate buy/sell signals using strategy<a class="headerlink" href="#generate-buysell-signals-using-strategy" title="Permanent link">&para;</a></h1>
<p>df = strategy.analyze_ticker(candles, {'pair': pair})
df.tail()
```</p>
<h3 id="display-the-trade-details">Display the trade details<a class="headerlink" href="#display-the-trade-details" title="Permanent link">&para;</a></h3>
<ul>
<li>Note that using <code>data.head()</code> would also work, however most indicators have some "startup" data at the top of the dataframe.</li>
<li>Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in <code>crossed*()</code> functions with completely different units</li>
<li>Comparison with full backtest
* having 200 buy signals as output for one pair from <code>analyze_ticker()</code> does not necessarily mean that 200 trades will be made during backtesting.
* Assuming you use only one condition such as, <code>df['rsi'] &lt; 30</code> as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns &gt; 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. </li>
</ul>
<p>```python</p>
<h1 id="report-results">Report results<a class="headerlink" href="#report-results" title="Permanent link">&para;</a></h1>
<p>print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=False)
data.tail()
```</p>
<h2 id="load-existing-objects-into-a-jupyter-notebook">Load existing objects into a Jupyter notebook<a class="headerlink" href="#load-existing-objects-into-a-jupyter-notebook" title="Permanent link">&para;</a></h2>
<p>The following cells assume that you have already generated data using the cli.<br />
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.</p>
<h3 id="load-backtest-results-to-pandas-dataframe">Load backtest results to pandas dataframe<a class="headerlink" href="#load-backtest-results-to-pandas-dataframe" title="Permanent link">&para;</a></h3>
<p>Analyze a trades dataframe (also used below for plotting)</p>
<p>```python
from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats</p>
<h1 id="if-backtest_dir-points-to-a-directory-itll-automatically-load-the-last-backtest-file">if backtest_dir points to a directory, it'll automatically load the last backtest file.<a class="headerlink" href="#if-backtest_dir-points-to-a-directory-itll-automatically-load-the-last-backtest-file" title="Permanent link">&para;</a></h1>
<p>backtest_dir = config["user_data_dir"] / "backtest_results"</p>
<h1 id="backtest_dir-can-also-point-to-a-specific-file">backtest_dir can also point to a specific file<a class="headerlink" href="#backtest_dir-can-also-point-to-a-specific-file" title="Permanent link">&para;</a></h1>
<h1 id="backtest_dir-configuser_data_dir-backtest_resultsbacktest-result-2020-07-01_20-04-22json">backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"<a class="headerlink" href="#backtest_dir-configuser_data_dir-backtest_resultsbacktest-result-2020-07-01_20-04-22json" title="Permanent link">&para;</a></h1>
<p>```</p>
<p>```python</p>
<h1 id="you-can-get-the-full-backtest-statistics-by-using-the-following-command">You can get the full backtest statistics by using the following command.<a class="headerlink" href="#you-can-get-the-full-backtest-statistics-by-using-the-following-command" title="Permanent link">&para;</a></h1>
<h1 id="this-contains-all-information-used-to-generate-the-backtest-result">This contains all information used to generate the backtest result.<a class="headerlink" href="#this-contains-all-information-used-to-generate-the-backtest-result" title="Permanent link">&para;</a></h1>
<p>stats = load_backtest_stats(backtest_dir)</p>
<p>strategy = 'SampleStrategy'</p>
<h1 id="all-statistics-are-available-per-strategy-so-if-strategy-list-was-used-during-backtest-this-will-be-reflected-here-as-well">All statistics are available per strategy, so if <code>--strategy-list</code> was used during backtest, this will be reflected here as well.<a class="headerlink" href="#all-statistics-are-available-per-strategy-so-if-strategy-list-was-used-during-backtest-this-will-be-reflected-here-as-well" title="Permanent link">&para;</a></h1>
<h1 id="example-usages">Example usages:<a class="headerlink" href="#example-usages" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy'][strategy]['results_per_pair'])</p>
<h1 id="get-pairlist-used-for-this-backtest">Get pairlist used for this backtest<a class="headerlink" href="#get-pairlist-used-for-this-backtest" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy'][strategy]['pairlist'])</p>
<h1 id="get-market-change-average-change-of-all-pairs-from-start-to-end-of-the-backtest-period">Get market change (average change of all pairs from start to end of the backtest period)<a class="headerlink" href="#get-market-change-average-change-of-all-pairs-from-start-to-end-of-the-backtest-period" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy'][strategy]['market_change'])</p>
<h1 id="maximum-drawdown">Maximum drawdown ()<a class="headerlink" href="#maximum-drawdown" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy'][strategy]['max_drawdown'])</p>
<h1 id="maximum-drawdown-start-and-end">Maximum drawdown start and end<a class="headerlink" href="#maximum-drawdown-start-and-end" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy'][strategy]['drawdown_start'])
print(stats['strategy'][strategy]['drawdown_end'])</p>
<h1 id="get-strategy-comparison-only-relevant-if-multiple-strategies-were-compared">Get strategy comparison (only relevant if multiple strategies were compared)<a class="headerlink" href="#get-strategy-comparison-only-relevant-if-multiple-strategies-were-compared" title="Permanent link">&para;</a></h1>
<p>print(stats['strategy_comparison'])</p>
<p>```</p>
<p>```python</p>
<h1 id="load-backtested-trades-as-dataframe">Load backtested trades as dataframe<a class="headerlink" href="#load-backtested-trades-as-dataframe" title="Permanent link">&para;</a></h1>
<p>trades = load_backtest_data(backtest_dir)</p>
<h1 id="show-value-counts-per-pair">Show value-counts per pair<a class="headerlink" href="#show-value-counts-per-pair" title="Permanent link">&para;</a></h1>
<p>trades.groupby("pair")["sell_reason"].value_counts()
```</p>
<h2 id="plotting-daily-profit-equity-line">Plotting daily profit / equity line<a class="headerlink" href="#plotting-daily-profit-equity-line" title="Permanent link">&para;</a></h2>
<p>```python</p>
<h1 id="plotting-equity-line-starting-with-0-on-day-1-and-adding-daily-profit-for-each-backtested-day">Plotting equity line (starting with 0 on day 1 and adding daily profit for each backtested day)<a class="headerlink" href="#plotting-equity-line-starting-with-0-on-day-1-and-adding-daily-profit-for-each-backtested-day" title="Permanent link">&para;</a></h1>
<p>from freqtrade.configuration import Configuration
from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats
import plotly.express as px
import pandas as pd</p>
<h1 id="strategy-samplestrategy">strategy = 'SampleStrategy'<a class="headerlink" href="#strategy-samplestrategy" title="Permanent link">&para;</a></h1>
<h1 id="config-configurationfrom_filesuser_dataconfigjson">config = Configuration.from_files(["user_data/config.json"])<a class="headerlink" href="#config-configurationfrom_filesuser_dataconfigjson" title="Permanent link">&para;</a></h1>
<h1 id="backtest_dir-configuser_data_dir-backtest_results">backtest_dir = config["user_data_dir"] / "backtest_results"<a class="headerlink" href="#backtest_dir-configuser_data_dir-backtest_results" title="Permanent link">&para;</a></h1>
<p>stats = load_backtest_stats(backtest_dir)
strategy_stats = stats['strategy'][strategy]</p>
<p>dates = []
profits = []
for date_profit in strategy_stats['daily_profit']:
dates.append(date_profit[0])
profits.append(date_profit[1])</p>
<p>equity = 0
equity_daily = []
for daily_profit in profits:
equity_daily.append(equity)
equity += float(daily_profit)</p>
<p>df = pd.DataFrame({'dates': dates,'equity_daily': equity_daily})</p>
<p>fig = px.line(df, x="dates", y="equity_daily")
fig.show()</p>
<p>```</p>
<h3 id="load-live-trading-results-into-a-pandas-dataframe">Load live trading results into a pandas dataframe<a class="headerlink" href="#load-live-trading-results-into-a-pandas-dataframe" title="Permanent link">&para;</a></h3>
<p>In case you did already some trading and want to analyze your performance</p>
<p>```python
from freqtrade.data.btanalysis import load_trades_from_db</p>
<h1 id="fetch-trades-from-database">Fetch trades from database<a class="headerlink" href="#fetch-trades-from-database" title="Permanent link">&para;</a></h1>
<p>trades = load_trades_from_db("sqlite:///tradesv3.sqlite")</p>
<h1 id="display-results">Display results<a class="headerlink" href="#display-results" title="Permanent link">&para;</a></h1>
<p>trades.groupby("pair")["sell_reason"].value_counts()
```</p>
<h2 id="analyze-the-loaded-trades-for-trade-parallelism">Analyze the loaded trades for trade parallelism<a class="headerlink" href="#analyze-the-loaded-trades-for-trade-parallelism" title="Permanent link">&para;</a></h2>
<p>This can be useful to find the best <code>max_open_trades</code> parameter, when used with backtesting in conjunction with <code>--disable-max-market-positions</code>.</p>
<p><code>analyze_trade_parallelism()</code> returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.</p>
<p>```python
from freqtrade.data.btanalysis import analyze_trade_parallelism</p>
<h1 id="analyze-the-above">Analyze the above<a class="headerlink" href="#analyze-the-above" title="Permanent link">&para;</a></h1>
<p>parallel_trades = analyze_trade_parallelism(trades, '5m')</p>
<p>parallel_trades.plot()
```</p>
<h2 id="plot-results">Plot results<a class="headerlink" href="#plot-results" title="Permanent link">&para;</a></h2>
<p>Freqtrade offers interactive plotting capabilities based on plotly.</p>
<p>```python
from freqtrade.plot.plotting import generate_candlestick_graph</p>
<h1 id="limit-graph-period-to-keep-plotly-quick-and-reactive">Limit graph period to keep plotly quick and reactive<a class="headerlink" href="#limit-graph-period-to-keep-plotly-quick-and-reactive" title="Permanent link">&para;</a></h1>
<h1 id="filter-trades-to-one-pair">Filter trades to one pair<a class="headerlink" href="#filter-trades-to-one-pair" title="Permanent link">&para;</a></h1>
<p>trades_red = trades.loc[trades['pair'] == pair]</p>
<p>data_red = data['2019-06-01':'2019-06-10']</p>
<h1 id="generate-candlestick-graph">Generate candlestick graph<a class="headerlink" href="#generate-candlestick-graph" title="Permanent link">&para;</a></h1>
<p>graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades_red,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)</p>
<p>```</p>
<p>```python</p>
<h1 id="show-graph-inline">Show graph inline<a class="headerlink" href="#show-graph-inline" title="Permanent link">&para;</a></h1>
<h1 id="graphshow">graph.show()<a class="headerlink" href="#graphshow" title="Permanent link">&para;</a></h1>
<h1 id="render-graph-in-a-seperate-window">Render graph in a seperate window<a class="headerlink" href="#render-graph-in-a-seperate-window" title="Permanent link">&para;</a></h1>
<p>graph.show(renderer="browser")</p>
<p>```</p>
<h2 id="plot-average-profit-per-trade-as-distribution-graph">Plot average profit per trade as distribution graph<a class="headerlink" href="#plot-average-profit-per-trade-as-distribution-graph" title="Permanent link">&para;</a></h2>
<p>```python
import plotly.figure_factory as ff</p>
<p>hist_data = [trades.profit_ratio]
group_labels = ['profit_ratio'] # name of the dataset</p>
<p>fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01)
fig.show()</p>
<p>```</p>
<p>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.</p>
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