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Add samples for plotting to jupyter documentation

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Matthias 2019-09-21 10:57:16 +02:00
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53
docs/data-analysis.md
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@ -61,34 +61,6 @@ except:
print(Path.cwd()) print(Path.cwd())
``` ```
## Load existing objects into a Jupyter notebook
These examples assume that you have already generated data using the cli. 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.
### 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_results/backtest-result.json")
# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()
```
### 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()
```
### Load multiple configuration files ### Load multiple configuration files
This option can be useful to inspect the results of passing in multiple configs. This option can be useful to inspect the results of passing in multiple configs.
@ -114,30 +86,9 @@ Best avoid relative paths, since this starts at the storage location of the jupy
} }
``` ```
### Load exchange data to a pandas dataframe ### Further Data analysis documentation
This loads candle data to a dataframe * [Strategy debugging](strategy_analysis_example.md) - also available as Jupyter notebook (`user_data/notebooks/strategy_analysis_example.ipynb`)
```python
from pathlib import Path
from freqtrade.data.history import load_pair_history
# Load data using values passed to function
ticker_interval = "5m"
data_location = Path('user_data', 'data', 'bitrex')
pair = "BTC_USDT"
candles = load_pair_history(datadir=data_location,
ticker_interval=ticker_interval,
pair=pair)
# Confirm success
print(f"Loaded len(candles) rows of data for {pair} from {data_location}")
candles.head()
```
Further Data analysis documents:
* [Strategy debugging](strategy_analysis_example.md)
* [Plotting](plotting.md) * [Plotting](plotting.md)
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. 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.

69
docs/strategy_analysis_example.md
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@ -68,8 +68,75 @@ df.tail()
```python ```python
# Report results # Report results
print(f"Generated {df['buy'].sum()} buy signals") print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=True) data = df.set_index('date', drop=False)
data.tail() data.tail()
``` ```
## Load existing objects into a Jupyter notebook
The following cells assume that you have already generated data using the cli.
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.
### Load backtest results to pandas dataframe
Analyze a trades dataframe (also used below for plotting)
```python
from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
trades = load_backtest_data(user_data_dir / "backtest_results/backtest-result.json")
# Show value-counts per pair
trades.groupby("pair")["sell_reason"].value_counts()
```
### Load live trading results into a pandas dataframe
In case you did already some trading and want to analyze your performance
```python
from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
trades = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results
trades.groupby("pair")["sell_reason"].value_counts()
```
## Plot results
Freqtrade offers interactive plotting capabilities based on plotly.
```python
from freqtrade.plot.plotting import generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive
data_red = data['2019-06-01':'2019-06-10']
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
data=data_red,
trades=trades,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)
```
```python
# Show graph inline
# graph.show()
# Render graph in a seperate window
graph.show(renderer="browser")
```
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. 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.

103
user_data/notebooks/strategy_analysis_example.ipynb
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@ -107,10 +107,111 @@
"source": [ "source": [
"# Report results\n", "# Report results\n",
"print(f\"Generated {df['buy'].sum()} buy signals\")\n", "print(f\"Generated {df['buy'].sum()} buy signals\")\n",
"data = df.set_index('date', drop=True)\n", "data = df.set_index('date', drop=False)\n",
"data.tail()" "data.tail()"
] ]
}, },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load existing objects into a Jupyter notebook\n",
"\n",
"The following cells assume that you have already generated data using the cli. \n",
"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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load backtest results to pandas dataframe\n",
"\n",
"Analyze a trades dataframe (also used below for plotting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_backtest_data\n",
"\n",
"# Load backtest results\n",
"trades = load_backtest_data(user_data_dir / \"backtest_results/backtest-result.json\")\n",
"\n",
"# Show value-counts per pair\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load live trading results into a pandas dataframe\n",
"\n",
"In case you did already some trading and want to analyze your performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.data.btanalysis import load_trades_from_db\n",
"\n",
"# Fetch trades from database\n",
"trades = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
"\n",
"# Display results\n",
"trades.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot results\n",
"\n",
"Freqtrade offers interactive plotting capabilities based on plotly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from freqtrade.plot.plotting import generate_candlestick_graph\n",
"# Limit graph period to keep plotly quick and reactive\n",
"\n",
"data_red = data['2019-06-01':'2019-06-10']\n",
"# Generate candlestick graph\n",
"graph = generate_candlestick_graph(pair=pair,\n",
" data=data_red,\n",
" trades=trades,\n",
" indicators1=['sma20', 'ema50', 'ema55'],\n",
" indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
" )\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show graph inline\n",
"# graph.show()\n",
"\n",
"# Render graph in a seperate window\n",
"graph.show(renderer=\"browser\")\n"
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},