stable/docs/data-analysis.md

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# 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
### Load backtest results into a pandas dataframe
```python
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from freqtrade.data.btanalysis import load_backtest_data
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# Load backtest results
df = load_backtest_data("user_data/backtest_data/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
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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
``` python
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from freqtrade.data.btanalysis import load_trades_from_db
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# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
df.groupby("pair")["sell_reason"].value_counts()
```
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### Load multiple configuration files
This option can be usefull to inspect the results of passing in multiple configs in case of problems
``` python
from freqtrade.configuration import Configuration
config = Configuration.from_files(["config1.json", "config2.json"])
print(config)
```
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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
```python
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# Imports
from pathlib import Path
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import os
from freqtrade.data.history import load_pair_history
from freqtrade.resolvers import StrategyResolver
# You can override strategy settings as demonstrated below.
# Customize these according to your needs.
# Define some constants
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ticker_interval = "5m"
# Name of the strategy class
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strategy_name = 'AwesomeStrategy'
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# Path to user data
user_data_dir = 'user_data'
# Location of the strategy
strategy_location = Path(user_data_dir, 'strategies')
# Location of the data
data_location = Path(user_data_dir, 'data', 'binance')
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# Pair to analyze
# Only use one pair here
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pair = "BTC_USDT"
```
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### Load exchange data
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```python
# Load data using values set above
bt_data = load_pair_history(datadir=Path(data_location),
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ticker_interval=ticker_interval,
pair=pair)
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# Confirm success
print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
```
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### Load and run strategy
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* Rerun each time the strategy file is changed
```python
# Load strategy using values set above
strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy
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# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(bt_data, {'pair': pair})
```
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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
* 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.
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.
```python
# Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=True)
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.