# Stock Analysis
Package for making elements of technical analysis of a stock easier. This package is meant to be a starting point for you to develop your own. As such, all the instructions for installing/setup will be assuming you will continue to develop on your end.
## Usage
This section will show some of the functionality of each class; however, it is by no means exhaustive.
### Getting data
```python
from stock_analysis import StockReader
reader = StockReader("2017-01-01", "2018-12-31")
# get bitcoin data in USD
bitcoin = reader.get_bitcoin_data("USD")
# get faang data
fb, aapl, amzn, nflx, goog = (
reader.get_ticker_data(ticker) for ticker in ["FB", "AAPL", "AMZN", "NFLX", "GOOG"]
)
# get S&P 500 data
sp = reader.get_index_data("S&P 500")
```
### Grouping data
```python
from stock_analysis import group_stocks, describe_group
faang = group_stocks(
{"Facebook": fb, "Apple": aapl, "Amazon": amzn, "Netflix": nflx, "Google": goog}
)
# describe the group
describe_group(faang)
```
### Building a portfolio
Groups assets by date and sums columns to build a portfolio.
```python
from stock_analysis import make_portfolio
faang_portfolio = make_portfolio(faang)
```
### Visualizing data
Be sure to check out the other methods here for different plot types, reference lines, shaded regions, and more!
#### Single asset
Evolution over time:
```python
import matplotlib.pyplot as plt
from stock_analysis import StockVisualizer
netflix_viz = StockVisualizer(nflx)
ax = netflix_viz.evolution_over_time(
"close", figsize=(10, 4), legend=False, title="Netflix closing price over time"
)
netflix_viz.add_reference_line(
ax,
x=nflx.high.idxmax(),
color="k",
linestyle=":",
label=f"highest value ({nflx.high.idxmax():%b %d})",
alpha=0.5,
)
ax.set_ylabel("price ($)")
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/netflix_line_plot.png" align="center" width="600" alt="line plot with reference line">
After hours trades:
```python
netflix_viz.after_hours_trades()
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/netflix_after_hours_trades.png" align="center" width="800" alt="after hours trades plot">
Differential in closing price versus another asset:
```python
netflix_viz.fill_between_other(fb)
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/nflx_vs_fb_closing_price.png" align="center" width="600" alt="differential between NFLX and FB">
Candlestick plots with resampling (uses `mplfinance`):
```python
netflix_viz.candlestick(
resample="2W", volume=True, xrotation=90, datetime_format="%Y-%b -"
)
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/candlestick.png" align="center" width="600" alt="resampled candlestick plot">
*Note: run `help()` on `StockVisualizer` for more visualizations*
#### Asset groups
Correlation heatmap:
```python
from stock_analysis import AssetGroupVisualizer
faang_viz = AssetGroupVisualizer(faang)
faang_viz.heatmap(True)
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/faang_heatmap.png" align="center" width="450" alt="correlation heatmap">
*Note: run `help()` on `AssetGroupVisualizer` for more visualizations. This object has many of the visualizations of the `StockVisualizer` class.*
### Analyzing data
Below are a few of the metrics you can calculate.
#### Single asset
```python
from stock_analysis import StockAnalyzer
nflx_analyzer = stock_analysis.StockAnalyzer(nflx)
nflx_analyzer.annualized_volatility()
```
#### Asset group
Methods of the `StockAnalyzer` class can be accessed by name with the `AssetGroupAnalyzer` class's `analyze()` method.
```python
from stock_analysis import AssetGroupAnalyzer
faang_analyzer = AssetGroupAnalyzer(faang)
faang_analyzer.analyze("annualized_volatility")
faang_analyzer.analyze("beta")
```
### Modeling
```python
from stock_analysis import StockModeler
```
#### Time series decomposition
```python
decomposition = StockModeler.decompose(nflx, 20)
fig = decomposition.plot()
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/nflx_ts_decomposition.png" align="center" width="450" alt="time series decomposition">
#### ARIMA
Build the model:
```python
arima_model = StockModeler.arima(nflx, 10, 1, 5)
```
Check the residuals:
```python
StockModeler.plot_residuals(arima_model)
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/arima_residuals.png" align="center" width="650" alt="ARIMA residuals">
Plot the predictions:
```python
arima_ax = StockModeler.arima_predictions(
arima_model, start=start, end=end, df=nflx, ax=axes[0], title="ARIMA"
)
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/arima_predictions.png" align="center" width="450" alt="ARIMA predictions">
#### Linear regression
Build the model:
```python
X, Y, lm = StockModeler.regression(nflx)
```
Check the residuals:
```python
StockModeler.plot_residuals(lm)
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/lm_residuals.png" align="center" width="650" alt="linear regression residuals">
Plot the predictions:
```python
linear_reg = StockModeler.regression_predictions(
lm, start=start, end=end, df=nflx, ax=axes[1], title="Linear Regression"
)
plt.show()
```
<img src="https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/lm_predictions.png" align="center" width="450" alt="linear regression predictions">
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"description": "\n# Stock Analysis\n\nPackage for making elements of technical analysis of a stock easier. This package is meant to be a starting point for you to develop your own. As such, all the instructions for installing/setup will be assuming you will continue to develop on your end.\n\n## Usage\n\nThis section will show some of the functionality of each class; however, it is by no means exhaustive.\n\n### Getting data\n\n```python\nfrom stock_analysis import StockReader\n\nreader = StockReader(\"2017-01-01\", \"2018-12-31\")\n\n# get bitcoin data in USD\nbitcoin = reader.get_bitcoin_data(\"USD\")\n\n# get faang data\nfb, aapl, amzn, nflx, goog = (\n reader.get_ticker_data(ticker) for ticker in [\"FB\", \"AAPL\", \"AMZN\", \"NFLX\", \"GOOG\"]\n)\n\n# get S&P 500 data\nsp = reader.get_index_data(\"S&P 500\")\n```\n\n### Grouping data\n\n```python\nfrom stock_analysis import group_stocks, describe_group\n\nfaang = group_stocks(\n {\"Facebook\": fb, \"Apple\": aapl, \"Amazon\": amzn, \"Netflix\": nflx, \"Google\": goog}\n)\n\n# describe the group\ndescribe_group(faang)\n```\n\n### Building a portfolio\n\nGroups assets by date and sums columns to build a portfolio.\n\n```python\nfrom stock_analysis import make_portfolio\n\nfaang_portfolio = make_portfolio(faang)\n```\n\n### Visualizing data\n\nBe sure to check out the other methods here for different plot types, reference lines, shaded regions, and more!\n\n#### Single asset\n\nEvolution over time:\n\n```python\nimport matplotlib.pyplot as plt\nfrom stock_analysis import StockVisualizer\n\nnetflix_viz = StockVisualizer(nflx)\n\nax = netflix_viz.evolution_over_time(\n \"close\", figsize=(10, 4), legend=False, title=\"Netflix closing price over time\"\n)\nnetflix_viz.add_reference_line(\n ax,\n x=nflx.high.idxmax(),\n color=\"k\",\n linestyle=\":\",\n label=f\"highest value ({nflx.high.idxmax():%b %d})\",\n alpha=0.5,\n)\nax.set_ylabel(\"price ($)\")\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/netflix_line_plot.png\" align=\"center\" width=\"600\" alt=\"line plot with reference line\">\n\nAfter hours trades:\n\n```python\nnetflix_viz.after_hours_trades()\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/netflix_after_hours_trades.png\" align=\"center\" width=\"800\" alt=\"after hours trades plot\">\n\nDifferential in closing price versus another asset:\n\n```python\nnetflix_viz.fill_between_other(fb)\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/nflx_vs_fb_closing_price.png\" align=\"center\" width=\"600\" alt=\"differential between NFLX and FB\">\n\nCandlestick plots with resampling (uses `mplfinance`):\n\n```python\nnetflix_viz.candlestick(\n resample=\"2W\", volume=True, xrotation=90, datetime_format=\"%Y-%b -\"\n)\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/candlestick.png\" align=\"center\" width=\"600\" alt=\"resampled candlestick plot\">\n\n*Note: run `help()` on `StockVisualizer` for more visualizations*\n\n#### Asset groups\n\nCorrelation heatmap:\n\n```python\nfrom stock_analysis import AssetGroupVisualizer\n\nfaang_viz = AssetGroupVisualizer(faang)\nfaang_viz.heatmap(True)\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/faang_heatmap.png\" align=\"center\" width=\"450\" alt=\"correlation heatmap\">\n\n*Note: run `help()` on `AssetGroupVisualizer` for more visualizations. This object has many of the visualizations of the `StockVisualizer` class.*\n\n### Analyzing data\n\nBelow are a few of the metrics you can calculate.\n\n#### Single asset\n\n```python\nfrom stock_analysis import StockAnalyzer\n\nnflx_analyzer = stock_analysis.StockAnalyzer(nflx)\nnflx_analyzer.annualized_volatility()\n```\n\n#### Asset group\n\nMethods of the `StockAnalyzer` class can be accessed by name with the `AssetGroupAnalyzer` class's `analyze()` method.\n\n```python\nfrom stock_analysis import AssetGroupAnalyzer\n\nfaang_analyzer = AssetGroupAnalyzer(faang)\nfaang_analyzer.analyze(\"annualized_volatility\")\n\nfaang_analyzer.analyze(\"beta\")\n```\n\n### Modeling\n\n```python\nfrom stock_analysis import StockModeler\n```\n\n#### Time series decomposition\n\n```python\ndecomposition = StockModeler.decompose(nflx, 20)\nfig = decomposition.plot()\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/nflx_ts_decomposition.png\" align=\"center\" width=\"450\" alt=\"time series decomposition\">\n\n#### ARIMA\n\nBuild the model:\n\n```python\narima_model = StockModeler.arima(nflx, 10, 1, 5)\n```\n\nCheck the residuals:\n\n```python\nStockModeler.plot_residuals(arima_model)\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/arima_residuals.png\" align=\"center\" width=\"650\" alt=\"ARIMA residuals\">\n\nPlot the predictions:\n\n```python\narima_ax = StockModeler.arima_predictions(\n arima_model, start=start, end=end, df=nflx, ax=axes[0], title=\"ARIMA\"\n)\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/arima_predictions.png\" align=\"center\" width=\"450\" alt=\"ARIMA predictions\">\n\n#### Linear regression\n\nBuild the model:\n\n```python\nX, Y, lm = StockModeler.regression(nflx)\n```\n\nCheck the residuals:\n\n```python\nStockModeler.plot_residuals(lm)\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/lm_residuals.png\" align=\"center\" width=\"650\" alt=\"linear regression residuals\">\n\nPlot the predictions:\n\n```python\nlinear_reg = StockModeler.regression_predictions(\n lm, start=start, end=end, df=nflx, ax=axes[1], title=\"Linear Regression\"\n)\nplt.show()\n```\n\n<img src=\"https://raw.githubusercontent.com/themagicalmammal/stock-analyser/main/images/lm_predictions.png\" align=\"center\" width=\"450\" alt=\"linear regression predictions\">\n",
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