From d8165652fcc92d32011e7781bcb0cfc693d67537 Mon Sep 17 00:00:00 2001 From: Markus Bilz Date: Mon, 4 Dec 2023 10:15:44 +0100 Subject: [PATCH] Deployed 599e356 with MkDocs version: 1.5.3 --- 404.html | 21 +- index.html | 149 ++++++++++++- naming_conventions/index.html | 382 ++++++++++++++++++++++++++++++++++ reference/index.html | 36 +++- search/search_index.json | 2 +- sitemap.xml | 9 +- sitemap.xml.gz | Bin 218 -> 237 bytes 7 files changed, 581 insertions(+), 18 deletions(-) create mode 100644 naming_conventions/index.html diff --git a/404.html b/404.html index 1e0ccf6..123f350 100644 --- a/404.html +++ b/404.html @@ -145,7 +145,7 @@
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  • + + Naming conventions + +
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    karelze/tclf @@ -197,7 +197,7 @@
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    karelze/tclf @@ -248,6 +248,27 @@
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      +
    • + + Installation + + +
      • + +
    • + + Minimal Example + + +
      • + +
    • + + Advanced Example + + +
      • +
    • Supported Algorithms @@ -340,15 +397,89 @@ -

      Trade classification for python

      -

      tclf is scikit-learn-compatible implementation of popular trade classification algorithms to classify financial markets transactions into buyer- and seller-initiated trades.

      +

      Trade classification for python 🐍

      +

      GitHubActions +Codecov

      +

      tclf is a scikit-learn-compatible implementation of trade classification algorithms to classify financial markets transactions into buyer- and seller-initiated trades.

      +

      The key features are:

      +
        +
      • Easy: Easy to use and learn.
        • +
      • Sklearn-compatible: Compatible to the sklearn API. Use sklearn metrics and visualizations.
        • +
      • Feature complete: Wide range of supported algorithms. Use the algorithms individually or stack them like LEGO blocks.
        • +
      +

      Installation

      +
      $ pip install .
+---> 100%
+Successfully installed tclf-0.0.0
+
      +

      Minimal Example

      +

      Let's start off simple: classify all trades by the quote rule and all other trades, which cannot be classified by the quote rule, randomly.

      +

      Create a main.py with: +

      import numpy as np
+import pandas as pd
+
+from tclf.classical_classifier import ClassicalClassifier
+
+X = pd.DataFrame(
+    [
+        [1.5, 1, 3],
+        [2.5, 1, 3],
+        [1.5, 3, 1],
+        [2.5, 3, 1],
+        [1, np.nan, 1],
+        [3, np.nan, np.nan],
+    ],
+    columns=["trade_price", "bid_ex", "ask_ex"],
+)
+y = pd.Series([1, 1, 1, 1, 1, 1])
+
+clf = ClassicalClassifier(layers=[("quote", "ex")], strategy="random")
+clf.fit(X, y)
+probs = clf.predict_proba(X)
+print(probs)
+
      +Run your script with +
      python main.py
+
      +In this example, input data is available as a pd.DataFrame/Series with columns conforming to our naming conventions.

      +

      The parameter layers=[("quote", "ex")] sets the quote rule at the exchange level and strategy="random" specifies the fallback strategy for unclassified trades. The true label y is not used in classification and only for API consistency by convention.

      +

      Advanced Example

      +

      Often it is desirable to classify both on exchange level data and nbbo data. Also, data might only be available as a numpy array. So let's extend the previous example by classifying using the quote rule at exchange level, then at nbbo and all other trades randomly.

      +

      import numpy as np
+from sklearn.metrics import accuracy_score
+
+from tclf.classical_classifier import ClassicalClassifier
+
+X = np.array(
+    [
+        [1.5, 1, 3, 2, 2.5],
+        [2.5, 1, 3, 1, 3],
+        [1.5, 3, 1, 1, 3],
+        [2.5, 3, 1, 1, 3],
+        [1, np.nan, 1, 1, 3],
+        [3, np.nan, np.nan, 1, 3],
+    ]
+)
+y_true = np.array([-1, 1, 1, -1, -1, 1])
+features = ["trade_price", "bid_ex", "ask_ex", "bid_best", "ask_best"]
+
+clf = ClassicalClassifier(
+    layers=[("quote", "ex"), ("quote", "best")], strategy="const", features=features
+)
+clf.fit(X, y_true)
+
+y_pred = clf.predict(X)
+print(accuracy_score(y_true, y_pred))
+
      +In this example, input data is available as np.arrays with both exchange ("ex") and nbbo data ("best"). We set the layers parameter to layers=[("quote", "ex"), ("quote", "best")] to classify trades first on subset "ex" and remaining trades on subset "best". Additionally, we have to set ClassicalClassifier(..., features=features) to pass column information to the classifier.

      +

      Like before, column/feature names must follow our naming conventions.

      Supported Algorithms

        -
      • Tick test
        • +
      • (Rev.) Tick test
      • Quote rule
        • -
      • LR algorithm
        • -
      • EMO rule
        • -
      • CLNV rule
        • +
      • (Rev.) LR algorithm
        • +
      • (Rev.) EMO rule
        • +
      • (Rev.) CLNV rule
      • Depth rule
      • Tradesize rule
      @@ -424,7 +555,7 @@

      References - + diff --git a/naming_conventions/index.html b/naming_conventions/index.html new file mode 100644 index 0000000..0d8dd25 --- /dev/null +++ b/naming_conventions/index.html @@ -0,0 +1,382 @@ + + + + + + + + + + + + + + + + + + + + Naming conventions - tclf + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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      Naming conventions

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