Alphas and Executors

This page explains Alpha Models and Executors in OlympusTrader, how to implement them, and how to use them in your strategies. Links to the relevant base classes are also included for reference.

Alpha Models

Alpha models are responsible for generating trade ideas (“insights”) based on your strategy’s logic, technical indicators, or other signals. Each alpha model should inherit from OlympusTrader.alpha.base_alpha.BaseAlpha.

Typical Alpha Model responsibilities: - Analyze historical and/or live data - Generate buy/sell/hold signals (insights) with confidence scores, expiry, etc. - Specify required indicators and their parameters

Example Alpha Model:

from OlympusTrader.alpha.base_alpha import BaseAlpha

class MyAlpha(BaseAlpha):
    def generate(self, symbol, data):
        if data['close'][-1] > data['close'].rolling(20).mean()[-1]:
            return self.create_insight(symbol, direction='long', confidence=0.8)
        return None

Adding Alpha Models to a Strategy:

from OlympusTrader.alpha.ema_price_crossover import EMAPriceCrossoverAlpha
strategy.add_alphas([
    EMAPriceCrossoverAlpha(strategy, emaPeriod=9),
])

Base class reference: - OlympusTrader.alpha.base_alpha.BaseAlpha

Executors

Executors are responsible for handling the execution and life cycle of insights. They determine how and when to submit orders, manage open positions, apply risk management, and handle state transitions (e.g., from NEW to FILLED, REJECTED, CLOSED, etc.). Executors inherit from OlympusTrader.insight.executors.base_executor.BaseExecutor.

Typical Executor responsibilities: - Submit orders to the broker when an insight is generated - Manage stop-loss, take-profit, and other exit logic - Handle order fills, rejections, and cancellations - Apply custom risk or portfolio management logic

Example Executor:

from OlympusTrader.insight.executors.filled.basicTakeProfit import BasicTakeProfitExecutor
strategy.add_executors([
    BasicTakeProfitExecutor(strategy, take_profit=0.05),
])

You can add multiple executors to your strategy, each handling different insight states or trading scenarios. Executors are run after your executeInsight function and can be customized or extended as needed.

Strategy start and init Examples

The start method is called once at the beginning of your strategy and is the recommended place to add your alphas and executors. The init method is called once per asset in your universe and is useful for per-asset setup (e.g., initializing state or loading data).

from OlympusTrader.alpha.ema_price_crossover import EMAPriceCrossoverAlpha
from OlympusTrader.insight.executors.filled.basicTakeProfit import BasicTakeProfitExecutor
from OlympusTrader import Strategy

class MyStrategy(Strategy):
    def start(self):
        # Add alphas and executors here
        self.add_alphas([
            EMAPriceCrossoverAlpha(self, emaPeriod=9),
        ])
        self.add_executors([
            BasicTakeProfitExecutor(self, take_profit=0.05),
        ])

    def init(self, asset):
        # Per-asset setup, called once for each asset in your universe
        self.state[asset] = {'custom_var': 0}
        # You can also load historical data, set up indicators, etc.

Base class reference: - OlympusTrader.insight.executors.base_executor.BaseExecutor