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125 lines
4.2 KiB
Markdown
125 lines
4.2 KiB
Markdown
# Analyzing bot data
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You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at `user_data/notebooks/analysis_example.ipynb`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation).
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*Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
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## Example snippets
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### Load backtest results into a pandas dataframe
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```python
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from freqtrade.data.btanalysis import load_backtest_data
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# Load backtest results
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df = load_backtest_data("user_data/backtest_data/backtest-result.json")
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# Show value-counts per pair
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df.groupby("pair")["sell_reason"].value_counts()
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```
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This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.
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### Load live trading results into a pandas dataframe
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``` python
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from freqtrade.data.btanalysis import load_trades_from_db
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# Fetch trades from database
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df = load_trades_from_db("sqlite:///tradesv3.sqlite")
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# Display results
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df.groupby("pair")["sell_reason"].value_counts()
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```
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### Load multiple configuration files
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This option can be usefull to inspect the results of passing in multiple configs in case of problems
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``` python
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from freqtrade.configuration import Configuration
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config = Configuration.from_files(["config1.json", "config2.json"])
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print(config)
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```
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## Strategy debugging example
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Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
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### Import requirements and define variables used in analyses
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```python
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# Imports
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from pathlib import Path
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import os
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from freqtrade.data.history import load_pair_history
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from freqtrade.resolvers import StrategyResolver
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# You can override strategy settings as demonstrated below.
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# Customize these according to your needs.
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# Define some constants
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ticker_interval = "5m"
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# Name of the strategy class
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strategy_name = 'AwesomeStrategy'
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# Path to user data
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user_data_dir = 'user_data'
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# Location of the strategy
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strategy_location = Path(user_data_dir, 'strategies')
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# Location of the data
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data_location = Path(user_data_dir, 'data', 'binance')
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# Pair to analyze
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# Only use one pair here
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pair = "BTC_USDT"
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```
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### Load exchange data
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```python
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# Load data using values set above
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bt_data = load_pair_history(datadir=Path(data_location),
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ticker_interval=ticker_interval,
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pair=pair)
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# Confirm success
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print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
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```
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### Load and run strategy
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* Rerun each time the strategy file is changed
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```python
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# Load strategy using values set above
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strategy = StrategyResolver({'strategy': strategy_name,
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'user_data_dir': user_data_dir,
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'strategy_path': strategy_location}).strategy
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# Generate buy/sell signals using strategy
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df = strategy.analyze_ticker(bt_data, {'pair': pair})
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```
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### Display the trade details
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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.
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#### Some possible problems
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* Columns with NaN values at the end of the dataframe
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* Columns used in `crossed*()` functions with completely different units
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#### Comparison with full backtest
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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.
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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).
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The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.
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```python
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# Report results
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print(f"Generated {df['buy'].sum()} buy signals")
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data = df.set_index('date', drop=True)
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data.tail()
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```
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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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