Strategy Building Guide

Learn how to build, customize, and optimize automated trading strategies in Nimbus

Introduction to Strategy Building

Building effective automated trading strategies requires a combination of market understanding, technical analysis, risk management, and systematic testing. This guide walks you through the complete process of creating custom strategies in Nimbus.

Strategy Fundamentals

Core concepts and principles of strategy design

Technical Implementation

Practical steps to build and deploy strategies

Optimization Techniques

Methods to improve strategy performance

Risk Integration

Incorporating risk management into strategies

Strategy Development Process

Phase 1: Strategy Conceptualization

Market Hypothesis

Define your core market belief or edge - Trend Following: Markets exhibit momentum that can be captured - Mean Reversion: Prices tend to return to historical averages - Arbitrage: Price differences exist between related assets - Market Making: Provide liquidity to capture bid-ask spreads - Event-Driven: React to specific market events or announcements

Strategy Classification

Choose your strategy type and timeframe mermaid graph TD A[Strategy Types] --> B[Trend Following] A --> C[Mean Reversion] A --> D[Market Making] A --> E[Arbitrage] B --> B1[Momentum] B --> B2[Breakout] B --> B3[Moving Average] C --> C1[RSI Based] C --> C2[Bollinger Bands] C --> C3[Support/Resistance] D --> D1[Grid Trading] D --> D2[Order Book] E --> E1[Statistical Arbitrage] E --> E2[Cross-Exchange]

Asset Selection

Choose appropriate assets for your strategy - Liquidity Requirements: Ensure sufficient trading volume - Volatility Preferences: Match strategy to volatility characteristics - Correlation Analysis: Consider relationships between assets - Market Hours: Account for trading session overlaps - Fundamental Factors: Include macro and news considerations

Phase 2: Technical Specification

Before implementing, clearly define your strategy’s entry, exit, and risk management rules.

Entry Conditions

Define precise conditions for entering positions:

Exit Conditions

Profit Taking

Methods to lock in profits - Fixed Targets: Predetermined price levels - Trailing Stops: Dynamic exit following price movements - Time-Based Exits: Maximum holding periods - Signal Reversal: Exit when opposite signal appears - Volatility-Based: Exit based on market volatility changes

Loss Management

Protecting capital from adverse moves - Stop Loss Orders: Fixed percentage or dollar amount losses - Maximum Adverse Excursion: Limit maximum unrealized losses - Portfolio Heat: Limit total portfolio risk exposure - Correlation Stops: Exit when asset correlations change - Time Stops: Exit after predetermined time periods

Implementation in Nimbus

Using the Strategy Builder

Always start with paper trading to validate your strategy before risking real capital.

Step 1: Strategy Setup

1. Navigate to Strategy Builder in your Nimbus dashboard
2. Select Strategy Template based on your chosen approach
3. Configure Basic Parameters:
   • Strategy name and description
   • Asset selection (single or multi-asset)
   • Base timeframe for signals
   • Initial capital allocation

Step 2: Signal Configuration

Technical Indicators

Configure technical analysis components

{
  "indicators": {
    "sma_20": {
      "type": "simple_moving_average",
      "period": 20
    },
    "rsi_14": {
      "type": "rsi",
      "period": 14,
      "overbought": 70,
      "oversold": 30
    },
    "bb_20": {
      "type": "bollinger_bands",
      "period": 20,
      "std_dev": 2
    }
  }
}

Entry Logic

Define entry conditions using logical operators

{
  "entry_conditions": {
    "long": {
      "operator": "AND",
      "conditions": [
        {"indicator": "price", "operator": ">", "value": "sma_20"},
        {"indicator": "rsi_14", "operator": "<", "value": 70},
        {"indicator": "volume", "operator": ">", "value": "volume_sma_10"}
      ]
    },
    "short": {
      "operator": "AND", 
      "conditions": [
        {"indicator": "price", "operator": "<", "value": "sma_20"},
        {"indicator": "rsi_14", "operator": ">", "value": 30}
      ]
    }
  }
}

Exit Logic

Configure exit conditions and risk management

{
  "exit_conditions": {
    "take_profit": {
      "type": "percentage",
      "value": 0.05
    },
    "stop_loss": {
      "type": "percentage", 
      "value": 0.02
    },
    "trailing_stop": {
      "type": "percentage",
      "value": 0.01,
      "activation_profit": 0.02
    }
  }
}

Advanced Strategy Features

Position Sizing Rules

Proper position sizing is crucial for long-term strategy success and risk management.

{
  "position_sizing": {
    "method": "kelly_criterion",
    "parameters": {
      "max_position_size": 0.1,
      "min_position_size": 0.01,
      "win_rate": 0.55,
      "avg_win_loss_ratio": 1.5,
      "kelly_fraction": 0.25
    }
  }
}

Dynamic Parameter Adjustment

Market Regime Detection

Adjust strategy parameters based on market conditions - Volatility Regimes: Different parameters for high/low volatility - Trend Identification: Adapt to trending vs sideways markets - Volume Analysis: Modify behavior based on market participation - Correlation Monitoring: Adjust when correlations break down

Performance-Based Adjustments

Modify strategy based on recent performance - Drawdown Management: Reduce position sizes during drawdowns - Winning Streak Handling: Manage risk during hot streaks - Signal Quality Assessment: Adjust based on signal accuracy - Market Impact Consideration: Reduce size if moving markets

Strategy Testing and Validation

Backtesting Framework

Comprehensive backtesting helps validate strategy effectiveness before live deployment.

Historical Data Analysis

Performance Metrics

Return Metrics

• Total Return: Absolute and percentage gains - Compound Annual Growth Rate (CAGR) - Risk-Adjusted Returns (Sharpe, Sortino Ratios) - Maximum Drawdown and Recovery Time - Win Rate and Profit Factor

Risk Metrics

• Value at Risk (VaR) at different confidence levels - Expected Shortfall (Conditional VaR) - Beta vs Market correlation and systematic risk - Standard Deviation of returns - Skewness and Kurtosis of return distribution

Trade-Level Analysis

• Average Trade Duration and holding periods - Trade Frequency and turnover analysis - Slippage and Transaction Cost impact - Market Impact of strategy trades - Capacity Analysis for strategy scalability

Paper Trading Validation

Before live deployment, test your strategy in paper trading mode:

Paper trading should simulate real-world conditions including slippage, latency, and market impact.

Strategy Optimization Techniques

Parameter Optimization

Grid Search

Systematic testing of parameter combinations - Exhaustive Search: Test all possible combinations - Computational Efficiency: Balance thoroughness with speed - Cross-Validation: Validate results across different periods - Overfitting Prevention: Avoid excessive parameter tuning

Genetic Algorithms

Evolutionary approach to parameter optimization - Population-Based Search: Multiple parameter sets evolve - Fitness Function: Define optimization objectives - Mutation and Crossover: Create new parameter combinations - Convergence Criteria: Determine when optimization is complete

Bayesian Optimization

Probabilistic approach to parameter tuning - Prior Distributions: Incorporate domain knowledge - Acquisition Functions: Efficiently explore parameter space - Uncertainty Quantification: Account for parameter uncertainty - Sequential Design: Iteratively improve parameter estimates

Multi-Objective Optimization

Balance competing objectives in strategy design:

Consider multiple objectives like return, risk, and drawdown rather than optimizing for returns alone.

1. Return Maximization vs Risk Minimization
2. Profit Factor vs Maximum Drawdown
3. Win Rate vs Average Win Size
4. Strategy Capacity vs Alpha Generation
5. Simplicity vs Performance

Risk Management Integration

Portfolio-Level Risk Controls

Position Sizing

Determine appropriate position sizes for each trade - Kelly Criterion: Optimal bet sizing based on edge - Risk Parity: Equal risk contribution from each position - Volatility Scaling: Size inversely proportional to volatility - Maximum Risk per Trade: Fixed percentage limits

Correlation Management

Monitor and control correlation between strategies - Maximum Correlation Limits: Prevent concentration risk - Dynamic Correlation Monitoring: Track changing relationships - Diversification Requirements: Maintain portfolio balance - Risk Budget Allocation: Distribute risk across strategies

Drawdown Controls

Protect against significant losses - Maximum Drawdown Limits: Portfolio and strategy level - Risk Scaling: Reduce size during drawdown periods - Circuit Breakers: Halt trading during extreme conditions - Recovery Protocols: Structured re-entry procedures

Common Strategy Patterns

Trend Following Strategies

Moving Average Crossover

{
  "strategy_type": "trend_following",
  "signals": {
    "long_entry": "fast_ma > slow_ma",
    "short_entry": "fast_ma < slow_ma",
    "exit": "signal_reversal"
  },
  "parameters": {
    "fast_period": 10,
    "slow_period": 20,
    "confirmation_bars": 2
  }
}

Breakout Strategy

{
  "strategy_type": "breakout",
  "signals": {
    "long_entry": "close > resistance_level",
    "short_entry": "close < support_level", 
    "exit": "return_to_range"
  },
  "parameters": {
    "lookback_period": 20,
    "breakout_threshold": 0.02,
    "volume_confirmation": true
  }
}

Mean Reversion Strategies

RSI Mean Reversion

{
  "strategy_type": "mean_reversion",
  "signals": {
    "long_entry": "rsi < oversold_level",
    "short_entry": "rsi > overbought_level",
    "exit": "rsi_return_to_neutral"
  },
  "parameters": {
    "rsi_period": 14,
    "oversold_level": 30,
    "overbought_level": 70
  }
}

Bollinger Band Reversal

{
  "strategy_type": "mean_reversion",
  "signals": {
    "long_entry": "close < lower_band",
    "short_entry": "close > upper_band",
    "exit": "close_crosses_middle_band"
  },
  "parameters": {
    "bb_period": 20,
    "bb_std_dev": 2,
    "rsi_filter": true
  }
}

Strategy Monitoring and Maintenance

Performance Monitoring

Maintenance Schedule

Daily Reviews

• Performance vs expectations check - Risk limit compliance verification - Signal generation quality assessment - Market condition changes evaluation

Weekly Analysis

• Detailed performance attribution - Parameter drift detection - Correlation matrix updates - Risk-adjusted return analysis

Monthly Optimization

• Strategy parameter review - Market regime analysis - Benchmark comparison study - Capacity and scalability assessment

Advanced Topics

Machine Learning Integration

Incorporate ML techniques to enhance traditional strategies with adaptive and predictive capabilities.

Feature Engineering

• Technical Indicators: Traditional and custom indicators - Market Microstructure: Order book and trade data - Alternative Data: Sentiment, news, social media - Cross-Asset Features: Correlations and spreads

Model Selection

• Classification: Signal generation (buy/sell/hold) - Regression: Price target prediction - Clustering: Market regime identification - Reinforcement Learning: Adaptive strategy optimization

Implementation Considerations

• Data Leakage Prevention: Proper train/test splitting - Feature Importance: Understanding model drivers - Model Stability: Robustness across market conditions - Online Learning: Adaptive model updating

Next Steps

Risk Management Best Practices

Learn advanced risk management techniques

Backtesting Engine

Test your strategies with historical data

Market Analysis Guide

Understand market dynamics for better strategies

Trading Wizard

Get AI-powered strategy recommendations