learning_rate=0.1,
n_estimators=2,
learning_rate=0.1,
n_jobs=-1,
colsample_bytree=0.05
)
# training the model
model.fit(X_train.iloc[:,2:], y_train.iloc[:,2:])
# saving model
model_pathname = Path(model_directory_path) / "model.joblib"
print(f"Saving model in {model_pathname}")
joblib.dump(model, model_pathname)
def infer(X_test: pd.DataFrame, model_directory_path: str = "resources") ->
pd.DataFrame:
# loading the model saved by the train function at previous iteration
model = joblib.load(Path(model_directory_path) / "model.joblib")
# creating the predicted label dataframe with correct dates and ids
y_test_predicted = X_test[["date", "id"]].copy()
y_test_predicted["value"] = model.predict(X_test.iloc[:, 2:])
return y_test_predicted
This groundbreaking Crunch has been designed by an exceptional team of renowned experts: 2021 Nobel Laureate in Economics Prof. Guido Imbens, Horst Simon, former Deputy Director of the Berkeley Lab, and Prof. Marcos Lopez de Prado, Global Head of Quantitative R&D at ADIA.
Uncovering the causal relationships between variables is a crucial step towards making AI systems more transparent and truly intelligent. By discovering the underlying causal structure from observational data, Crunch helps its customers to move beyond mere correlation and enable machines to understand the 'why' behind phenomena. This Crunch expects the community models to unveil the causal directed acyclic graph (DAG) that governs sets of variables, pushing the boundaries of AI's reasoning capabilities. Such advancements are vital across numerous fields, including healthcare, economics, social sciences, and environmental studies.
X Alpha deploys capital in early and mid-stage disruptive companies across the US and western Europe.
For the first time ever, a leading industry expert with a 15-year proven track record collaborates with a Community of more than 5,000 data scientists and 600 PhDs. This first of a kind partnership aims to create a groundbreaking AI-driven prototype for venture capital.
VC firms often make investment decisions based on insufficient information and heavily depend on human intuition and biased decision-making.
The CrunchDAO Machine Learning Community will identify trends, relationships and hidden patterns leading to replicable, reproducible and unbiased Alpha-generating process for Venture Capital.
DataCrunch leverages CrunchDAO's quantitative research to manage its systematic market-neutral portfolio. The proprietary dataset encompasses thousands of publicly traded U.S. companies, providing a comprehensive view of the market landscape.
To achieve this, DataCrunch requires the community to build algorithms that can predict the relative performance of assets within an investment universe. Specifically, the community models should precisely rank the constituents of this very universe.
Successful models are expected to demonstrate consistent performance, out-of-sample, offering valuable insights that can be integrated into any systematic investment process.
"I'm very excited to see what the participants are going to come up with, because if they come up with useful things, that's going to be very impactful"
"Crowdsourcing has a very important role to play in investing. Firms turn investing problems into forecasting problems, then outsource to global researchers"
"Institutional finance hasn’t yet had disruption, but likely will; specifically with respect to the competition for research talent in the years to come"
