tclf is scikit-learn-compatible implementation of popular trade classification algorithms to classify financial markets transactions into buyer- and seller-initiated trades.
+
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:
+$ pip install .
+---> 100%
+Successfully installed tclf-0.0.0
+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)
+python main.py
+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.
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))
+"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.