stable/docs/data-analysis.md
2019-08-10 15:45:41 +02:00

115 lines
3.9 KiB
Markdown

# Analyzing bot data
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).
*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)*
## Example snippets
### Load backtest results into a pandas dataframe
```python
from freqtrade.data.btanalysis import load_backtest_data
# 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()
```
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.
### Load live trading results into a pandas dataframe
``` python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
df.groupby("pair")["sell_reason"].value_counts()
```
## Strategy debugging example
Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.
### Import requirements and define variables used in analyses
```python
# Imports
from pathlib import Path
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
ticker_interval = "5m"
# Name of the strategy class
strategy_name = 'AwesomeStrategy'
# 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')
# Pair to analyze
# Only use one pair here
pair = "BTC_USDT"
```
### Load exchange data
```python
# Load data using values set above
bt_data = load_pair_history(datadir=Path(data_location),
ticker_interval=ticker_interval,
pair=pair)
# Confirm success
print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")
```
### Load and run strategy
* 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
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(bt_data, {'pair': pair})
```
### Display the trade details
* Note that using `data.head()` would also work, however most indicators have some "startup" data at the top of the dataframe.
#### Some possible problems
* Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units
#### Comparison with full backtest
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()
```
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.