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edits to clarify backtesting analysis

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Jonathan Raviotta
2019-08-08 22:09:15 -04:00
parent 2bc67b4a96
commit ccf3c69874
3 changed files with 176 additions and 101 deletions
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docs/data-analysis.md
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@@ -2,11 +2,33 @@
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).
## Example snippets
### Load backtest results into a pandas dataframe
```python
# 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()
```
### Load live trading results into a pandas dataframe
``` python
# 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 the script
### Import requirements and define variables used in analyses
```python
# Imports
@@ -47,12 +69,6 @@ print("Loaded " + str(len(bt_data)) + f" rows of data for {pair} from {data_loca
### Load and run strategy
* Rerun each time the strategy file is changed
* 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
```python
# Load strategy using values set above
@@ -60,33 +76,31 @@ strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy
# Run strategy (just like in backtesting)
# 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()
```
### Load backtest results into a pandas dataframe
```python
# 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()
```
### Load live trading results into a pandas dataframe
``` python
# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
df.groupby("pair")["sell_reason"].value_counts()
```
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.