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MIROCANA ALPHA

prototype

RELEASE DATE: 1 MAY 2018

strategy example
alpha
class BollingerStrategy(BaseStrategy): DURATIONS = BASE_DURATIONS MAX_LAST = max(DURATIONS) * 14 K1 = [3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25] def apply_code(self, mqb, ctx): predictions = [] for duration in self.DURATIONS: # delta = np.mean(np.abs(mqb['delta'].last(14 * duration))) delta = np.mean(np.abs(mqb['delta'].last_with_duration(20, duration))) # ma14 = np.mean(mqb['close_ask'].last(14 * duration)) ma14 = np.mean(mqb['close_ask'].last_with_duration(20, duration)) for k1 in self.K1: if mqb['close_ask'][0] > ma14 + delta*k1: prediction = {'duration': duration, 'value': -1, 'k1': k1} predictions.append(prediction) elif mqb['close_ask'][0] < ma14 - delta*k1: prediction = {'duration': duration, 'value': 1, 'k1': k1} predictions.append(prediction) return predictions
instructions
Create, test and evaluate your own trading strategies with Mirocana core.
git clone https://github.com/mirocana/alpha.git
cd alpha
pip install virtualenv
virtualenv _env -p python3
. _env/bin/activate
pip install -r requirements.txt
python strategies/testing.py
create your strategy

A web platform for algo-traders and quantitative analysts where they will be able to create, backtest and evaluate their own strategy based on the data from Mirocana Data Sources. We develop our own syntax and internal API for optimized performance of the backtest.

We will reward the creators of strategies that find actual patterns in data with MIRO tokens. MIRO tokens can be used to access Mirocana products or exchange into other crypto-currencies on the Exchange.

Members of Mirocana Research Group will be the first people to join Mirocana Alpha product.