# Popular Libraries

## Tslearn

### Get Historical Data

Get some historical market data to train and test the model. For example, get data for the securities shown in the following table:

Group Name | Tickers |
---|---|

Overall US market | SPY, QQQ, DIA |

Tech companies | AAPL, MSFT, TSLA |

Long-term US Treasury ETFs | IEF, TLT |

Short-term US Treasury ETFs | SHV, SHY |

Heavy metal ETFs | GLD, IAU, SLV |

Energy sector | USO, XLE, XOM |

qb = QuantBook() tickers = ["SPY", "QQQ", "DIA", "AAPL", "MSFT", "TSLA", "IEF", "TLT", "SHV", "SHY", "GLD", "IAU", "SLV", "USO", "XLE", "XOM"] symbols = [qb.add_equity(ticker, Resolution.DAILY).symbol for ticker in tickers] history = qb.history(symbols, datetime(2020, 1, 1), datetime(2022, 2, 20))

### Prepare Data

You need some historical data to prepare the data for the model. If you have historical data, manipulate it to train and test the model. In this example, standardize the log close price time-series of the securities. Follow these steps to prepare the data:

- Unstack the historical DataFrame and select the close column.
- Take the logarithm of the historical time series.
- Standardize the data.

close = history.unstack(0).close

log_close = np.log(close)

Taking the logarithm eases the compounding effect.

standard_close = (log_close - log_close.mean()) / log_close.std()

### Train Models

Instead of using real-time comparison, we could apply a technique call Dynamic Time Wrapping (DTW) with Barycenter Averaging (DBA). Intuitively, it is a technique of averaging a few time-series into a single one without losing much of their information. Since not all time-series would move efficiently like in ideal EMH assumption, this would allow similarity analysis of different time-series with sticky lags. Check the technical details from tslearn documentation page.

We then can separate different clusters by KMean after DBA.

# Set up the Time Series KMean model with soft DBA. km = TimeSeriesKMeans(n_clusters=6, # We have 6 main groups metric="softdtw", # soft for differentiable random_state=0) # Fit the model. km.fit(standard_close.T)

### Test Models

We visualize the clusters and their corresponding underlying series.

- Predict with the label of the data.
- Create a class to aid plotting.
- Plot the results.
- Display the groupings.

labels = km.predict(standard_close.T)

def plot_helper(ts): # plot all points of the data set for i in range(ts.shape[0]): plt.plot(ts[i, :], "k-", alpha=.2) # plot the given barycenter of them barycenter = softdtw_barycenter(ts, gamma=1.) plt.plot(barycenter, "r-", linewidth=2)

j = 1 plt.figure(figsize=(15, 10)) for i in set(labels): # Select the series in the i-th cluster. X = standard_close.iloc[:, [n for n, k in enumerate(labels) if k == i]].values # Plot the series and barycenter-averaged series. plt.subplot(len(set(labels)) // 3 + (1 if len(set(labels))%3 != 0 else 0), 3, j) plt.title(f"Cluster {i+1}") plot_helper(X.T) j += 1 plt.show()

for i in set(labels): print(f"Cluster {i+1}: {standard_close.columns[[n for n, k in enumerate(labels) if k == i]]}")

### Store Models

You can save and load `tslearn`

models using the Object Store.

#### Save Models

Follow these steps to save models in the Object Store:

- Set the key name of the model to be stored in the Object Store.
- Call the
`GetFilePath`

`get_file_path`

method with the key. - Delete the current file to avoid a
`FileExistsError`

error when you save the model. - Call the
`to_hdf5`

method with the file path.

model_key = "model"

file_name = qb.object_store.get_file_path(model_key)

This method returns the file path where the model will be stored.

import os os.remove(file_name)

km.to_hdf5(file_name + ".hdf5")

#### Load Models

You must save a model into the Object Store before you can load it from the Object Store. If you saved a model, follow these steps to load it:

- Call the
`ContainsKey`

`contains_key`

method. - Call the
`GetFilePath`

`get_file_path`

method with the key. - Call the
`from_hdf5`

method with the file path.

qb.object_store.contains_key(model_key)

This method returns a boolean that represents if the `model_key`

is in the Object Store. If the Object Store does not contain the `model_key`

, save the model using the `model_key`

before you proceed.

file_name = qb.object_store.get_file_path(model_key)

This method returns the path where the model is stored.

loaded_model = TimeSeriesKMeans.from_hdf5(file_name + ".hdf5")

This method returns the saved model.