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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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70
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.

12
setup.py
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@ -25,7 +25,15 @@ develop = [
'pytest-random-order',
]
all_extra = api + plot + develop
jupyter = [
'jupyter',
'nbstripout',
'ipykernel',
'isort',
'yapf',
]
all_extra = api + plot + develop + jupyter
setup(name='freqtrade',
version=__version__,
@ -68,7 +76,7 @@ setup(name='freqtrade',
'dev': all_extra,
'plot': plot,
'all': all_extra,
'jupyter': [],
'jupyter': jupyter,
},
include_package_data=True,

195
user_data/notebooks/analysis_example.ipynb
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@ -4,31 +4,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strategy debugging example"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Change directory\n",
"# Define all paths relative to the project root shown in the cell output\n",
"import os\n",
"from pathlib import Path\n",
"try:\n",
"\tos.chdir(Path(os.getcwd(), '../..'))\n",
"\tprint(os.getcwd())\n",
"except:\n",
"\tpass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import requirements and define variables used in the script"
"# Analyzing bot data\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`. For usage instructions, see [jupyter.org](https://jupyter.org/documentation)."
]
},
{
@ -39,11 +17,97 @@
"source": [
"# Imports\n",
"from pathlib import Path\n",
"import os\n",
"from freqtrade.data.history import load_pair_history\n",
"from freqtrade.resolvers import StrategyResolver\n",
"from freqtrade.data.btanalysis import load_backtest_data\n",
"from freqtrade.data.btanalysis import load_trades_from_db\n",
"from freqtrade.data.btanalysis import load_trades_from_db"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Change directory\n",
"# Define all paths relative to the project root shown in the cell output\n",
"try:\n",
"\tos.chdir(Path(Path.cwd(), '../..'))\n",
"\tprint(Path.cwd())\n",
"except:\n",
"\tpass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example snippets"
]
},
{
"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",
"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",
"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": [
"## Strategy debugging example\n",
"\n",
"Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import requirements and define variables used in analyses"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define some constants\n",
"ticker_interval = \"1m\"\n",
"# Name of the strategy class\n",
@ -51,9 +115,9 @@
"# Path to user data\n",
"user_data_dir = 'user_data'\n",
"# Location of the strategy\n",
"strategy_location = Path(user_data_dir, 'strategies')\n",
"strategy_location = os.path.join(user_data_dir, 'strategies')\n",
"# Location of the data\n",
"data_location = Path(user_data_dir, 'data', 'binance')\n",
"data_location = os.path.join(user_data_dir, 'data', 'binance')\n",
"# Pair to analyze \n",
"# Only use one pair here\n",
"pair = \"BTC_USDT\""
@ -85,15 +149,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load and run strategy \n",
"\n",
"* Rerun each time the strategy file is changed\n",
"* 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.\n",
"\n",
"Some possible problems:\n",
"\n",
"* Columns with NaN values at the end of the dataframe\n",
"* Columns used in `crossed*()` functions with completely different units"
"### Load and run strategy\n",
"* Rerun each time the strategy file is changed"
]
},
{
@ -107,53 +164,49 @@
" 'user_data_dir': user_data_dir,\n",
" 'strategy_path': strategy_location}).strategy\n",
"\n",
"# Run strategy (just like in backtesting)\n",
"df = strategy.analyze_ticker(bt_data, {'pair': pair})\n",
"# Generate buy/sell signals using strategy\n",
"df = strategy.analyze_ticker(bt_data, {'pair': pair})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Display the trade details\n",
"* Note that using `data.head()` would also work, however most indicators have some \"startup\" data at the top of the dataframe.\n",
"\n",
"#### Some possible problems\n",
"\n",
"* Columns with NaN values at the end of the dataframe\n",
"* Columns used in `crossed*()` functions with completely different units\n",
"\n",
"#### Comparison with full backtest\n",
"\n",
"having 200 buy signals as output for one pair from `analyze_ticker()` does not necessarily mean that 200 trades will be made during backtesting.\n",
"\n",
"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).\n",
"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.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Report results\n",
"print(f\"Generated {df['buy'].sum()} buy signals\")\n",
"data = df.set_index('date', drop=True)\n",
"data.tail()"
]
},
{
"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",
"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()\n"
]
},
{
"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",
"df = load_trades_from_db(\"sqlite:///tradesv3.sqlite\")\n",
"\n",
"# Display results\n",
"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."
]
}
],