We Don't Just Trade Energy
We Engineer Solutions

We develop fully automated algorithmic trading strategies. This helps us parse large amounts of real-time data and make informed decisions effectively.

• Fully automated trading strategies
• Real-time data processing and decision-making
• Reduced operational overhead

def optimize_trade_execution(market_data, forecast):
    signal = generate_signal(market_data)
    if np.greater(signal.confidence, THRESHOLD):
        return execute_trade(signal, forecast)
    return None

def execute_trade(data, forecast):
    signal = analyze(data)
    if np.greater(signal.conf, THRESH):
        return trade(signal)
    return None

We use data and machine learning models to predict market movements.

• To support our edge in every decision
• Lowering balancing costs for end consumers
• To understand dynamics of a rapidly changing energy landscape

Our trading platform is cloud-native, built to parse diverse data in real time and support a wide range of automated strategies. We design our own infrastructure using modern tools, with Python at the core and flexibility where it counts.

• Robust systems built for live markets
• Real-time monitoring
• Infrastructure that scales with our needs

Robust risk controls are embedded throughout our system, ensuring responsible trading with comprehensive monitoring and fail-safes.

• Real-time risk monitoring
• Circuit breakers