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chore: ruff format notebook

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Matthias 2024-08-19 19:59:15 +02:00
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65
docs/strategy_analysis_example.md
View File

@ -18,13 +18,13 @@ from pathlib import Path
# 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"
i=0
i = 0
try:
os.chdir(project_root)
if not Path('LICENSE').is_file():
if not Path("LICENSE").is_file():
i = 0
while i < 4 and (not Path('LICENSE').is_file()):
os.chdir(Path(Path.cwd(), '../'))
while i < 4 and (not Path("LICENSE").is_file()):
os.chdir(Path(Path.cwd(), "../"))
i += 1
project_root = Path.cwd()
except FileNotFoundError:
@ -63,12 +63,13 @@ from freqtrade.data.history import load_pair_history
from freqtrade.enums import CandleType
candles = load_pair_history(datadir=data_location,
candles = load_pair_history(
datadir=data_location,
timeframe=config["timeframe"],
pair=pair,
data_format = "json", # Make sure to update this to your data
data_format="json", # Make sure to update this to your data
candle_type=CandleType.SPOT,
)
)
# Confirm success
print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
@ -90,7 +91,7 @@ strategy.dp = DataProvider(config, None, None)
strategy.ft_bot_start()
# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(candles, {'pair': pair})
df = strategy.analyze_ticker(candles, {"pair": pair})
df.tail()
```
@ -109,7 +110,7 @@ df.tail()
```python
# Report results
print(f"Generated {df['enter_long'].sum()} entry signals")
data = df.set_index('date', drop=False)
data = df.set_index("date", drop=False)
data.tail()
```
@ -141,25 +142,24 @@ backtest_dir = config["user_data_dir"] / "backtest_results"
# This contains all information used to generate the backtest result.
stats = load_backtest_stats(backtest_dir)
strategy = 'SampleStrategy'
strategy = "SampleStrategy"
# All statistics are available per strategy, so if `--strategy-list` was used during backtest,
# this will be reflected here as well.
# Example usages:
print(stats['strategy'][strategy]['results_per_pair'])
print(stats["strategy"][strategy]["results_per_pair"])
# Get pairlist used for this backtest
print(stats['strategy'][strategy]['pairlist'])
print(stats["strategy"][strategy]["pairlist"])
# Get market change (average change of all pairs from start to end of the backtest period)
print(stats['strategy'][strategy]['market_change'])
print(stats["strategy"][strategy]["market_change"])
# Maximum drawdown ()
print(stats['strategy'][strategy]['max_drawdown'])
print(stats["strategy"][strategy]["max_drawdown"])
# Maximum drawdown start and end
print(stats['strategy'][strategy]['drawdown_start'])
print(stats['strategy'][strategy]['drawdown_end'])
print(stats["strategy"][strategy]["drawdown_start"])
print(stats["strategy"][strategy]["drawdown_end"])
# Get strategy comparison (only relevant if multiple strategies were compared)
print(stats['strategy_comparison'])
print(stats["strategy_comparison"])
```
@ -189,14 +189,13 @@ from freqtrade.data.btanalysis import load_backtest_stats
# backtest_dir = config["user_data_dir"] / "backtest_results"
stats = load_backtest_stats(backtest_dir)
strategy_stats = stats['strategy'][strategy]
strategy_stats = stats["strategy"][strategy]
df = pd.DataFrame(columns=['dates','equity'], data=strategy_stats['daily_profit'])
df['equity_daily'] = df['equity'].cumsum()
df = pd.DataFrame(columns=["dates", "equity"], data=strategy_stats["daily_profit"])
df["equity_daily"] = df["equity"].cumsum()
fig = px.line(df, x="dates", y="equity_daily")
fig.show()
```
### Load live trading results into a pandas dataframe
@ -226,7 +225,7 @@ from freqtrade.data.btanalysis import analyze_trade_parallelism
# Analyze the above
parallel_trades = analyze_trade_parallelism(trades, '5m')
parallel_trades = analyze_trade_parallelism(trades, "5m")
parallel_trades.plot()
```
@ -243,19 +242,17 @@ from freqtrade.plot.plotting import generate_candlestick_graph
# Limit graph period to keep plotly quick and reactive
# Filter trades to one pair
trades_red = trades.loc[trades['pair'] == pair]
trades_red = trades.loc[trades["pair"] == pair]
data_red = data['2019-06-01':'2019-06-10']
data_red = data["2019-06-01":"2019-06-10"]
# Generate candlestick graph
graph = generate_candlestick_graph(pair=pair,
graph = generate_candlestick_graph(
pair=pair,
data=data_red,
trades=trades_red,
indicators1=['sma20', 'ema50', 'ema55'],
indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']
)
indicators1=["sma20", "ema50", "ema55"],
indicators2=["rsi", "macd", "macdsignal", "macdhist"],
)
```
@ -265,7 +262,6 @@ graph = generate_candlestick_graph(pair=pair,
# Render graph in a separate window
graph.show(renderer="browser")
```
## Plot average profit per trade as distribution graph
@ -276,11 +272,10 @@ import plotly.figure_factory as ff
hist_data = [trades.profit_ratio]
group_labels = ['profit_ratio'] # name of the dataset
group_labels = ["profit_ratio"] # name of the dataset
fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)
fig.show()
```
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.

66
freqtrade/templates/strategy_analysis_example.ipynb
View File

@ -34,13 +34,13 @@
"# 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",
"project_root = \"somedir/freqtrade\"\n",
"i=0\n",
"i = 0\n",
"try:\n",
" os.chdir(project_root)\n",
" if not Path('LICENSE').is_file():\n",
" if not Path(\"LICENSE\").is_file():\n",
" i = 0\n",
" while i < 4 and (not Path('LICENSE').is_file()):\n",
" os.chdir(Path(Path.cwd(), '../'))\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",
"except FileNotFoundError:\n",
@ -92,12 +92,13 @@
"from freqtrade.enums import CandleType\n",
"\n",
"\n",
"candles = load_pair_history(datadir=data_location,\n",
"candles = load_pair_history(\n",
" datadir=data_location,\n",
" timeframe=config[\"timeframe\"],\n",
" pair=pair,\n",
" data_format = \"json\", # Make sure to update this to your data\n",
" data_format=\"json\", # Make sure to update this to your data\n",
" candle_type=CandleType.SPOT,\n",
" )\n",
")\n",
"\n",
"# Confirm success\n",
"print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
@ -128,7 +129,7 @@
"strategy.ft_bot_start()\n",
"\n",
"# Generate buy/sell signals using strategy\n",
"df = strategy.analyze_ticker(candles, {'pair': pair})\n",
"df = strategy.analyze_ticker(candles, {\"pair\": pair})\n",
"df.tail()"
]
},
@ -155,7 +156,7 @@
"source": [
"# Report results\n",
"print(f\"Generated {df['enter_long'].sum()} entry signals\")\n",
"data = df.set_index('date', drop=False)\n",
"data = df.set_index(\"date\", drop=False)\n",
"data.tail()"
]
},
@ -205,24 +206,24 @@
"# This contains all information used to generate the backtest result.\n",
"stats = load_backtest_stats(backtest_dir)\n",
"\n",
"strategy = 'SampleStrategy'\n",
"strategy = \"SampleStrategy\"\n",
"# All statistics are available per strategy, so if `--strategy-list` was used during backtest,\n",
"# this will be reflected here as well.\n",
"# Example usages:\n",
"print(stats['strategy'][strategy]['results_per_pair'])\n",
"print(stats[\"strategy\"][strategy][\"results_per_pair\"])\n",
"# Get pairlist used for this backtest\n",
"print(stats['strategy'][strategy]['pairlist'])\n",
"print(stats[\"strategy\"][strategy][\"pairlist\"])\n",
"# Get market change (average change of all pairs from start to end of the backtest period)\n",
"print(stats['strategy'][strategy]['market_change'])\n",
"print(stats[\"strategy\"][strategy][\"market_change\"])\n",
"# Maximum drawdown ()\n",
"print(stats['strategy'][strategy]['max_drawdown'])\n",
"print(stats[\"strategy\"][strategy][\"max_drawdown\"])\n",
"# Maximum drawdown start and end\n",
"print(stats['strategy'][strategy]['drawdown_start'])\n",
"print(stats['strategy'][strategy]['drawdown_end'])\n",
"print(stats[\"strategy\"][strategy][\"drawdown_start\"])\n",
"print(stats[\"strategy\"][strategy][\"drawdown_end\"])\n",
"\n",
"\n",
"# Get strategy comparison (only relevant if multiple strategies were compared)\n",
"print(stats['strategy_comparison'])\n"
"print(stats[\"strategy_comparison\"])"
]
},
{
@ -265,13 +266,13 @@
"# backtest_dir = config[\"user_data_dir\"] / \"backtest_results\"\n",
"\n",
"stats = load_backtest_stats(backtest_dir)\n",
"strategy_stats = stats['strategy'][strategy]\n",
"strategy_stats = stats[\"strategy\"][strategy]\n",
"\n",
"df = pd.DataFrame(columns=['dates','equity'], data=strategy_stats['daily_profit'])\n",
"df['equity_daily'] = df['equity'].cumsum()\n",
"df = pd.DataFrame(columns=[\"dates\", \"equity\"], data=strategy_stats[\"daily_profit\"])\n",
"df[\"equity_daily\"] = df[\"equity\"].cumsum()\n",
"\n",
"fig = px.line(df, x=\"dates\", y=\"equity_daily\")\n",
"fig.show()\n"
"fig.show()"
]
},
{
@ -319,7 +320,7 @@
"\n",
"\n",
"# Analyze the above\n",
"parallel_trades = analyze_trade_parallelism(trades, '5m')\n",
"parallel_trades = analyze_trade_parallelism(trades, \"5m\")\n",
"\n",
"parallel_trades.plot()"
]
@ -345,18 +346,17 @@
"# Limit graph period to keep plotly quick and reactive\n",
"\n",
"# Filter trades to one pair\n",
"trades_red = trades.loc[trades['pair'] == pair]\n",
"trades_red = trades.loc[trades[\"pair\"] == pair]\n",
"\n",
"data_red = data['2019-06-01':'2019-06-10']\n",
"data_red = data[\"2019-06-01\":\"2019-06-10\"]\n",
"# Generate candlestick graph\n",
"graph = generate_candlestick_graph(pair=pair,\n",
"graph = generate_candlestick_graph(\n",
" pair=pair,\n",
" data=data_red,\n",
" trades=trades_red,\n",
" indicators1=['sma20', 'ema50', 'ema55'],\n",
" indicators2=['rsi', 'macd', 'macdsignal', 'macdhist']\n",
" )\n",
"\n",
"\n"
" indicators1=[\"sma20\", \"ema50\", \"ema55\"],\n",
" indicators2=[\"rsi\", \"macd\", \"macdsignal\", \"macdhist\"],\n",
")"
]
},
{
@ -369,7 +369,7 @@
"# graph.show()\n",
"\n",
"# Render graph in a separate window\n",
"graph.show(renderer=\"browser\")\n"
"graph.show(renderer=\"browser\")"
]
},
{
@ -389,10 +389,10 @@
"\n",
"\n",
"hist_data = [trades.profit_ratio]\n",
"group_labels = ['profit_ratio'] # name of the dataset\n",
"group_labels = [\"profit_ratio\"] # name of the dataset\n",
"\n",
"fig = ff.create_distplot(hist_data, group_labels, bin_size=0.01)\n",
"fig.show()\n"
"fig.show()"
]
},
{