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65
docs/data-analysis.md
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@ -5,19 +5,19 @@ You can analyze the results of backtests and trading history easily using Jupyte
## Pro tips ## Pro tips
* See [jupyter.org](https://jupyter.org/documentation) for usage instructions. * See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
* 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)* * Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
* Copy the example notebook so your changes don't get clobbered with the next freqtrade update. * Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
## Fine print ## Fine print
Some tasks don't work especially well in notebooks. For example, anything using asyncronous exectution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required parameters to functions. Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
## Recommended workflow ## Recommended workflow
| Task | Tool | | Task | Tool |
--- | --- --- | ---
Bot operations | CLI Bot operations | CLI
Repetative tasks | shell scripts Repetitive tasks | Shell scripts
Data analysis & visualization | Notebook Data analysis & visualization | Notebook
1. Use the CLI to 1. Use the CLI to
@ -28,26 +28,26 @@ Data analysis & visualization | Notebook
1. Collect these actions in shell scripts 1. Collect these actions in shell scripts
* save complicated commands with arguments * save complicated commands with arguments
* execute mult-step operations * execute multi-step operations
* automate testing strategies and prepareing data for analysis * automate testing strategies and preparing data for analysis
1. Use a notebook to 1. Use a notebook to
* import data * visualize data
* munge and plot to generate insights * munge and plot to generate insights
## Example utility snippets for Jupyter notebooks ## Example utility snippets
### Change directory to root ### Change directory to root
Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistant. Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.
```python ```python
# Change directory
# Modify this cell to insure that the output shows the correct path.
# Define all paths relative to the project root shown in the cell output
import os import os
from pathlib import Path from pathlib import Path
# Change directory
# Modify this cell to insure that the output shows the correct path.
# Define all paths relative to the project root shown in the cell output
project_root = "somedir/freqtrade" project_root = "somedir/freqtrade"
i=0 i=0
try: try:
@ -61,25 +61,16 @@ except:
print(Path.cwd()) print(Path.cwd())
``` ```
### Watch project for changes to code
This scans the project for changes to code before Jupyter runs cells.
```python
# Reloads local code changes
%load_ext autoreload
%autoreload 2
```
## Load existing objects into a Jupyter notebook ## Load existing objects into a Jupyter notebook
These examples assume that you have already generated data using the cli. These examples 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. 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 ### Load backtest results into a pandas dataframe
```python ```python
# Load backtest results
from freqtrade.data.btanalysis import load_backtest_data from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
df = load_backtest_data("user_data/backtest_data/backtest-result.json") df = load_backtest_data("user_data/backtest_data/backtest-result.json")
# Show value-counts per pair # Show value-counts per pair
@ -89,8 +80,9 @@ df.groupby("pair")["sell_reason"].value_counts()
### Load live trading results into a pandas dataframe ### Load live trading results into a pandas dataframe
``` python ``` python
# Fetch trades from database
from freqtrade.data.btanalysis import load_trades_from_db from freqtrade.data.btanalysis import load_trades_from_db
# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite") df = load_trades_from_db("sqlite:///tradesv3.sqlite")
# Display results # Display results
@ -102,12 +94,13 @@ df.groupby("pair")["sell_reason"].value_counts()
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
``` python ``` python
# Load config from multiple files import json
from freqtrade.configuration import Configuration from freqtrade.configuration import Configuration
# Load config from multiple files
config = Configuration.from_files(["config1.json", "config2.json"]) config = Configuration.from_files(["config1.json", "config2.json"])
# Show the config in memory # Show the config in memory
import json
print(json.dumps(config, indent=1)) print(json.dumps(config, indent=1))
``` ```
@ -116,10 +109,10 @@ print(json.dumps(config, indent=1))
This loads candle data to a dataframe This loads candle data to a dataframe
```python ```python
# Load data using values passed to function
from pathlib import Path from pathlib import Path
from freqtrade.data.history import load_pair_history from freqtrade.data.history import load_pair_history
# Load data using values passed to function
ticker_interval = "5m" ticker_interval = "5m"
data_location = Path('user_data', 'data', 'bitrex') data_location = Path('user_data', 'data', 'bitrex')
pair = "BTC_USDT" pair = "BTC_USDT"
@ -128,8 +121,8 @@ candles = load_pair_history(datadir=data_location,
pair=pair) pair=pair)
# Confirm success # Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}") print(f"Loaded len(candles) rows of data for {pair} from {data_location}")
display(candles.head()) candles.head()
``` ```
## Strategy debugging example ## Strategy debugging example
@ -160,17 +153,17 @@ pair = "BTC_USDT"
### Load exchange data ### Load exchange data
```python ```python
# Load data using values set above
from pathlib import Path from pathlib import Path
from freqtrade.data.history import load_pair_history from freqtrade.data.history import load_pair_history
# Load data using values set above
candles = load_pair_history(datadir=data_location, candles = load_pair_history(datadir=data_location,
ticker_interval=ticker_interval, ticker_interval=ticker_interval,
pair=pair) pair=pair)
# Confirm success # Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}") print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
display(candles.head()) candles.head()
``` ```
### Load and run strategy ### Load and run strategy
@ -178,8 +171,9 @@ display(candles.head())
* Rerun each time the strategy file is changed * Rerun each time the strategy file is changed
```python ```python
# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver from freqtrade.resolvers import StrategyResolver
# Load strategy using values set above
strategy = StrategyResolver({'strategy': strategy_name, strategy = StrategyResolver({'strategy': strategy_name,
'user_data_dir': user_data_dir, 'user_data_dir': user_data_dir,
'strategy_path': strategy_location}).strategy 'strategy_path': strategy_location}).strategy
@ -190,7 +184,7 @@ df = strategy.analyze_ticker(candles, {'pair': pair})
### Display the trade details ### 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. * Note that using `data.tail()` is preferable to `data.head()` as most indicators have some "startup" data at the top of the dataframe.
* Some possible problems * Some possible problems
* Columns with NaN values at the end of the dataframe * Columns with NaN values at the end of the dataframe
* Columns used in `crossed*()` functions with completely different units * Columns used in `crossed*()` functions with completely different units
@ -199,7 +193,6 @@ df = strategy.analyze_ticker(candles, {'pair': pair})
* 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. * 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 ```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=True)

289
user_data/notebooks/jupyter_snippets_example.ipynb
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@ -1,289 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analyzing bot data with Jupyter notebooks \n",
"\n",
"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`. \n",
"\n",
"## Pro tips \n",
"\n",
"* See [jupyter.org](https://jupyter.org/documentation) for usage instructions.\n",
"* 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)*\n",
"* Copy the example notebook so your changes don't get clobbered with the next freqtrade update.\n",
"\n",
"## Fine print \n",
"\n",
"Some tasks don't work especially well in notebooks. For example, anything using asyncronous exectution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required parameters to functions.\n",
"\n",
"## Recommended workflow \n",
"\n",
"| Task | Tool | \n",
" --- | --- \n",
"Bot operations | CLI \n",
"Repetative tasks | shell scripts\n",
"Data analysis & visualization | Notebook \n",
"\n",
"1. Use the CLI to\n",
" * download historical data\n",
" * run a backtest\n",
" * run with real-time data\n",
" * export results \n",
"\n",
"1. Collect these actions in shell scripts\n",
" * save complicated commands with arguments\n",
" * execute mult-step operations \n",
" * automate testing strategies and prepareing data for analysis\n",
"\n",
"1. Use a notebook to\n",
" * import data\n",
" * munge and plot to generate insights"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example utility snippets for Jupyter notebooks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Change directory to root \n",
"\n",
"Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistant."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Change directory\n",
"# Modify this cell to insure that the output shows the correct path.\n",
"# Define all paths relative to the project root shown in the cell output\n",
"import os\n",
"from pathlib import Path\n",
"\n",
"project_root = \"somedir/freqtrade\"\n",
"i=0\n",
"try:\n",
" os.chdirdir(project_root)\n",
" assert Path('LICENSE').is_file()\n",
"except:\n",
" while i<4 and (not Path('LICENSE').is_file()):\n",
" os.chdir(Path(Path.cwd(), '../'))\n",
" i+=1\n",
" project_root = Path.cwd()\n",
"print(Path.cwd())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Watch project for changes to code\n",
"This scans the project for changes to code before Jupyter runs cells."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Reloads local code changes\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load existing objects into a Jupyter notebook\n",
"\n",
"These examples assume that you have already generated data using the cli. These examples 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 into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load backtest results\n",
"from freqtrade.data.btanalysis import load_backtest_data\n",
"df = load_backtest_data(\"user_data/backtest_data/backtest-result.json\")\n",
"\n",
"# Show value-counts per pair\n",
"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load live trading results into a pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetch trades from database\n",
"from freqtrade.data.btanalysis import load_trades_from_db\n",
"df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
"\n",
"# Display results\n",
"df.groupby(\"pair\")[\"sell_reason\"].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load multiple configuration files\n",
"This option can be useful to inspect the results of passing in multiple configs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load config from multiple files\n",
"from freqtrade.configuration import Configuration\n",
"config = Configuration.from_files([\"config1.json\", \"config2.json\"])\n",
"\n",
"# Show the config in memory\n",
"import json\n",
"print(json.dumps(config, indent=1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load exchange data to a pandas dataframe\n",
"\n",
"This loads candle data to a dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Load data using values passed to function\n",
"from pathlib import Path\n",
"from freqtrade.data.history import load_pair_history\n",
"\n",
"ticker_interval = \"5m\"\n",
"data_location = Path('user_data', 'data', 'bitrex')\n",
"pair = \"BTC_USDT\"\n",
"candles = load_pair_history(datadir=data_location,\n",
" ticker_interval=ticker_interval,\n",
" pair=pair)\n",
"\n",
"# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"display(candles.head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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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"display_name": "Python 3",
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"title_cell": "Table of Contents",
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"library": "var_list.r",
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42
user_data/notebooks/strategy_analysis_example.ipynb
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@ -24,10 +24,10 @@
"source": [ "source": [
"# Change directory\n", "# Change directory\n",
"# Modify this cell to insure that the output shows the correct path.\n", "# Modify this cell to insure that the output shows the correct path.\n",
"# Define all paths relative to the project root shown in the cell output\n",
"import os\n", "import os\n",
"from pathlib import Path\n", "from pathlib import Path\n",
"\n", "\n",
"# Define all paths relative to the project root shown in the cell output\n",
"project_root = \"somedir/freqtrade\"\n", "project_root = \"somedir/freqtrade\"\n",
"i=0\n", "i=0\n",
"try:\n", "try:\n",
@ -38,11 +38,7 @@
" os.chdir(Path(Path.cwd(), '../'))\n", " os.chdir(Path(Path.cwd(), '../'))\n",
" i+=1\n", " i+=1\n",
" project_root = Path.cwd()\n", " project_root = Path.cwd()\n",
"print(Path.cwd())\n", "print(Path.cwd())"
"\n",
"# Reloads local code changes\n",
"%load_ext autoreload\n",
"%autoreload 2"
] ]
}, },
{ {
@ -69,9 +65,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'freqtrade'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-b421d7818902>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load data using values set above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpathlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPath\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfreqtrade\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_pair_history\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m candles = load_pair_history(datadir=data_location,\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'freqtrade'"
]
}
],
"source": [ "source": [
"# Load data using values set above\n", "# Load data using values set above\n",
"from pathlib import Path\n", "from pathlib import Path\n",
@ -83,7 +91,7 @@
"\n", "\n",
"# Confirm success\n", "# Confirm success\n",
"print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n", "print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
"display(candles.head())" "candles.head()"
] ]
}, },
{ {
@ -96,11 +104,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
"outputs": [], "outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'freqtrade'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-a217d0e0719c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load strategy using values set above\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mfreqtrade\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolvers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStrategyResolver\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m strategy = StrategyResolver({'strategy': strategy_name,\n\u001b[1;32m 4\u001b[0m \u001b[0;34m'user_data_dir'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0muser_data_dir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m 'strategy_path': strategy_location}).strategy\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'freqtrade'"
]
}
],
"source": [ "source": [
"# Load strategy using values set above\n", "# Load strategy using values set above\n",
"from freqtrade.resolvers import StrategyResolver\n", "from freqtrade.resolvers import StrategyResolver\n",