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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| format: | ||
| html: | ||
| code-fold: false | ||
| jupyter: python3 | ||
| execute: | ||
| cache: true # re-render only when source changes | ||
| --- | ||
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| # Candlestick Charts with `plotly` | ||
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| In financial applications, we often have access to OHLC data (containing the open, high, low, and close price on each day). We can use a candlestick chart can help us see the movement of the price within each day. | ||
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| To implement a [candlestick chart](https://plotly.com/python/candlestick-charts/), we can use the [`Candlestick` class](https://plotly.github.io/plotly.py-docs/generated/plotly.graph_objects.Candlestick.html) from plotly's Graph Objects sub-library. | ||
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| We start with some OHLC data: | ||
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| ```{python} | ||
| ohlc_data = [ | ||
| {"date": "2030-03-16", "open": 236.2800, "high": 240.0550, "low": 235.9400, "close": 237.7100, "volume": 28092196}, | ||
| {"date": "2030-03-15", "open": 234.9600, "high": 235.1850, "low": 231.8100, "close": 234.8100, "volume": 26042669}, | ||
| {"date": "2030-03-12", "open": 234.0100, "high": 235.8200, "low": 233.2300, "close": 235.7500, "volume": 22653662}, | ||
| {"date": "2030-03-11", "open": 234.9600, "high": 239.1700, "low": 234.3100, "close": 237.1300, "volume": 29907586}, | ||
| {"date": "2030-03-10", "open": 237.0000, "high": 237.0000, "low": 232.0400, "close": 232.4200, "volume": 29746812} | ||
| ] | ||
| ``` | ||
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| Mapping the data to get into a format the chart likes (separate lists): | ||
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| ```{python} | ||
| dates = [] | ||
| opens = [] | ||
| highs = [] | ||
| lows = [] | ||
| closes = [] | ||
| for item in ohlc_data: | ||
| dates.append(item["date"]) | ||
| opens.append(item["open"]) | ||
| highs.append(item["high"]) | ||
| lows.append(item["low"]) | ||
| closes.append(item["close"]) | ||
| ``` | ||
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| Finally, creating the chart: | ||
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| ```{python} | ||
| from plotly.graph_objects import Figure, Candlestick | ||
| stick = Candlestick(x=dates, open=opens, high=highs, low=lows, close=closes) | ||
| fig = Figure(data=[stick]) | ||
| fig.update_layout(title="Example Candlestick Chart") | ||
| fig.show() | ||
| ``` |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,92 @@ | ||
| --- | ||
| format: | ||
| html: | ||
| code-fold: false | ||
| jupyter: python3 | ||
| execute: | ||
| cache: true # re-render only when source changes | ||
| --- | ||
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| # Scatter Plot Trendlines w/ `plotly` | ||
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| In many cases, it may be helpful to add a trendline to a chart, to help examine relationships between variables. | ||
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| The [`scatter` function](https://plotly.com/python-api-reference/generated/plotly.express.scatter) in `plotly` is the only type of chart that supports trendlines. | ||
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| To illustrate how to add trendlines, let's revisit the previous scatter plot example: | ||
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| ```{python} | ||
| #| code-fold: true | ||
| scatter_data = [ | ||
| {"income": 30_000, "life_expectancy": 65.5}, | ||
| {"income": 35_000, "life_expectancy": 62.1}, | ||
| {"income": 50_000, "life_expectancy": 66.7}, | ||
| {"income": 55_000, "life_expectancy": 71.0}, | ||
| {"income": 70_000, "life_expectancy": 72.5}, | ||
| {"income": 75_000, "life_expectancy": 77.3}, | ||
| {"income": 90_000, "life_expectancy": 82.9}, | ||
| {"income": 95_000, "life_expectancy": 80.0}, | ||
| ] | ||
| incomes = [] | ||
| expectancies = [] | ||
| for item in scatter_data: | ||
| incomes.append(item["income"]) | ||
| expectancies.append(item["life_expectancy"]) | ||
| ``` | ||
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| ```{python} | ||
| from plotly.express import scatter | ||
| fig = scatter(x=incomes, y=expectancies, height=350, | ||
| title="Life Expectancy by Income", | ||
| labels={"x": "Income", "y": "Life Expectancy (years)"}, | ||
| ) | ||
| fig.show() | ||
| ``` | ||
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| Upon viewing the chart, looks like there may be evidence of a trend. | ||
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| ## Linear Trends | ||
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| The [`scatter` function](https://plotly.com/python-api-reference/generated/plotly.express.scatter) has some trend-line related parameters: | ||
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| ```{python} | ||
| from plotly.express import scatter | ||
| fig = scatter(x=incomes, y=expectancies, height=350, | ||
| title="Life Expectancy by Income", | ||
| labels={"x": "Income", "y": "Life Expectancy (years)"}, | ||
| trendline="ols", trendline_color_override="red" | ||
| ) | ||
| fig.show() | ||
| ``` | ||
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| :::{.callout-note title="FYI"} | ||
| Under the hood, `plotly` uses the `statsmodels` package to calculate the trend, so you may have to install that package as well. | ||
| ::: | ||
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| A linear trend assumes that there is a straight-line relationship between the independent and dependent variables. In the context of US GDP data, a linear trend suggests that GDP changes at a constant rate over time. When applying linear regression, the goal is to find the best-fit line that minimizes the residuals (differences between the predicted and actual values) under the assumption that the underlying relationship is linear. | ||
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| Linear regression is simple and interpretable but can be overly restrictive when the real-world data follows a more complex, non-linear pattern. | ||
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| ## Non-linear Trends | ||
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| In addition to the \"ols\" (Ordinary Least Squares) linear trend, we can use a \"lowess\" (Locally Weighted Scatterplot Smoothing) trend, which may be a better fit for non-linear relationships. | ||
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| ```{python} | ||
| from plotly.express import scatter | ||
| fig = scatter(x=incomes, y=expectancies, height=350, | ||
| title="Life Expectancy by Income", | ||
| labels={"x": "Income", "y": "Life Expectancy (years)"}, | ||
| trendline="lowess", trendline_color_override="red" | ||
| ) | ||
| fig.show() | ||
| ``` | ||
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| [LOWESS](https://en.wikipedia.org/wiki/Local_regression) is a [non-parametric method](https://www.investopedia.com/terms/n/nonparametric-statistics.asp) that fits multiple local regressions to different segments of the data. Instead of assuming a global linear relationship, it captures local patterns by fitting simple models in small neighborhoods around each point. These local models are then combined to create a smooth curve that adjusts to non-linearities in the data. A LOWESS trend can adapt to sudden changes, curves, and other complex behaviors in the data, making it ideal for datasets where the relationship between variables changes over time. |
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