Challenge Data

Prediction of Sharpe ratio for blends of quantitative strategies
by Napoleon X

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Competitive challenge
Economic sciences
Time series
10MB to 1GB
Intermediary level


Started on Jan. 1, 2019

Challenge context

Napoleon Crypto

November 20, 2018

Description of the Company

Napoleon Crypto (NC) is a company specialised in designing quantitative investment solutions, i.e. investment solutions based on algorithms. While NC has developed several performing algorithms (called strategies), it aims at creating a process optimising the allocation between these algorithms. NC has developed a platform where some of these strategies are published: NaPoleonX

Problem description

The problem is a prediction challenge that aims at helping the Company to build an optimal blend ofquantitative strategies, given a set of such strategies.
A quantitative strategy I can be represented by a time series I = (I_0, ..., I_t, ...) defined for eachtrading day, with I_n+1 = I_n × (1 + r_n+1) where r_n+1 being the performance of the strategy for the trading day n + 1. For any integer k > 0, k × I is also a representation of the quantitative strategy I.
The financial performance of a quantitative strategy can be measured by its Sharpe ratio over a period, which corresponds to its growth performance divided by its volatility on this period. As a consequence, given a set of quantitative strategies and given a period, it is almost always possible to create a linear combination of strategies (blend) whose Sharpe ratio will be better (higher) than the Sharpe ratio of each individual quantitative strategy.

Challenge goals

Goal description:

NC’s goal is to find the best allocation among its quantitative strategies every week (more or less 5 trading days), i.e.the combination for which the Sharpe ratio will be the highest over the next 5 trading days. In order to adapt this issue to a ML problem, we have decided to create a challenge consisting in predicting the Sharpe ratio S* of a given combination (w_1, ..., w_7) of strategies, where the Sharpe ratio is slightly modified to avoid near 0 volatility issues. Given the log returns log_return_formulas for each strategy i and time s, the Sharpe ratio of the combination (w_1, ..., w_7) is defined for all time t, as:


Data description

Data description

The input data contains a 7-uplet of weights, then 7 time series of 21 trading days, corresponding to 7 strategies, and then 3 time series of 21 trading days corresponding to 3 financial indicators. Input data, for training and testing, will be given by a .csv file, whose first line contains the header. Then each line corresponds to a sample, each column to a feature. The features are the following:
- ID: Id of the sample which is linked to the ID of the output file;
- (w_1 , ..., w_n ): a 7-uplet of weights ≥ 0 and summing to 1;
- I_1 to I_7: Values of strategies I_1 to I_7 for the past 21 trading days;
- X_1 to X_3: Values of 3 financial indicators for the past 21 trading days.

There will be 10 000 samples for the train set and 4 450 for the test set. For a given sample, the time series (for the 7 strategies and the 3 financial indicators) are given over the same 21 trading days. On a given set of 21 trading days, there could be up to 50 different samples (basically the 7-uplets are different, while the 10 time series are the same).

The training outputs are given in a .csv file. Each line corresponds to a sample:
- ID: Id of the sample;
- Target: value of the Sharpe ratio corresponding to the blend defined by the sample ID.

ID Target
0000 0.82
0001 1.20
0002 1.53
: :
9999 0.65

Metric and benchmark

For each model, the test outputs will be compared to the actual value of the Sharpe ratio of the corresponding blend. But in order to smoothen the extremes, we have decided to apply a function function_formulas to the results. The scoring function d between an output vector y_hat = (y_hat_1, ..., y_hat_N) and the real vector y = (y_1, ..., y_N) is defined as:


The lower the score, the better.


Please send any question to or

Benchmark description

Challenge ENS: Napoleon Benchmark


Arnaud Dartois and Stefan Duprey

Napoleon Crypto

November 20, 2018



In the simplest way possible we have chosen as benchmark the average of the training period: 1.200344.


You can find in the file the python code for our own metric, which correspond to a mean absolut error after applied a sigmoid function on target and prediction.


Training input/output and testing input/output are available in four files named respectively Trainig_Input.csv, Training_Output.csv, Testing_Input.csv and Testing\_Output.csv.


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The challenge provider


Napoleon Group is building the future of investing around three entities: • Napoleon AM has been granted an AIFM license and will be able to offer investment solutions for professional investors. Napoleon AM will hence propose crypto exposure solutions; • Napoleon Capital is a specialist in quantitative strategies issued from open-competition holding a financial advisor license CIF (Conseil en Investissement Financier/FSA equivalent) granted by ORIAS/AMF; • Napoleon Index is aiming to become registered for index publishing and administration under BMR regulation, blockchain based. Napoleon Group has a French DNA, with an international mindset and a strong will to comply with the highest standard regulations. Napoleon Group is betting on regulation to address institutional investors’ needs. AM industry evolution: The Group vision is to embrace 3 major developments that are reshaping the AM industry: • Quantitative finance is revolutionizing the financial industry through automation and Artificial Intelligence; • Blockchain simplifies many operational processes through increased speed, security, transparency and cost-efficiency; and • Tokenization is the real potential future of financial assets. Crypto assets are being adopted by institutional clients as a new asset class. This will lead to a world of programmable real and financial assets, allowing to trade in an ever more efficient market.