Strategy & Best Practices

This guide provides a framework for developing robust trading strategies. It is not a set of rules, but a collection of principles and best practices refined through rigorous empirical validation.

Core Strategy Principles

Embrace “Depth Over Breadth”

Our core philosophy is to master a select few methodologies rather than superficially using many. As Bruce Lee noted, “I fear not the man who has practiced 10,000 kicks once, but the man who has practiced one kick 10,000 times.” Apply this by:

Mastering one component, like WaveTrend 4D, before adding another. Perfecting a single strategy on one timeframe before diversifying. Focusing on the quality of your signals, not the quantity.

Our Philosophy Discover the story and values that guide our system design.

What Actually Works (The 80/20 of Strategy)

Our research consistently shows that successful strategies share these traits:

Simplicity > Complexity: Systems with fewer, well-understood parameters are more robust.
Regime Awareness: Adapting to market conditions (trending vs. ranging) is critical.
Risk Management First: How you manage risk and size positions has more impact than the perfect entry.
Consistency > Perfection: A good plan executed consistently is better than a perfect plan executed sporadically.
Defaults are Baselines: Our default parameters are an empirically tested starting point, not an afterthought.

Common Mistakes to Avoid

Over-optimizing: Searching for “perfect” parameters that only work on historical data. Stick to robust, round numbers (e.g., 0.5, not 0.47).
Indicator Overload: Using too many indicators creates noise, not clarity. Quality over quantity.
Ignoring Market Regime: Applying a trend-following strategy in a ranging market is a recipe for failure.
Chasing Signals: Forcing trades when your setup isn’t present. Let the market come to you.
Failing to Document: If you don’t track your results and parameter changes, you cannot learn and improve.

Market & Timeframe Selection

How to Configure for Different Markets

Our tools are market-agnostic, but different markets have unique personalities. Tune your parameters to respect their behaviour.

Personality: High volatility, strong momentum cycles, operates 24/7.

Signal Thresholds: Consider conservative thresholds to filter out noise.
Lookbacks: Shorter lookbacks (e.g., 21-55 bars) can be effective to capture fast-moving trends.
Risk: Use smaller position sizes and wider stops (e.g., ATR-based) to handle volatility.

How to Choose the Right Timeframe

Your timeframe should match your trading style and lifestyle.

Day Trading (Scalping): 1m, 5m, 15m. Requires constant attention. Use more sensitive settings.
Swing Trading: 1h, 4h, Daily. A good balance for most traders. Default parameters are often optimized for these timeframes.
Position Trading: Daily, Weekly. Requires patience. Use less sensitive settings and focus on major trends.

Building Your Strategy

How to Combine Multiple Signal Providers

The Flux Composer (Pro) allows you to aggregate up to 5 independent signal sources. This creates multilayered resilience. Common approaches:

“Confirmation Model”: Use a “fast” Signal Provider (e.g., WaveTrend 4D) as your primary signal and a slower, higher-timeframe Signal Provider as a confirmation filter.
“Multi-Factor Model”: Combine signals from different sources (e.g., different exchanges, markets or even non-price data).
“Parent Asset Filter”: For altcoins or stocks, use a signal from a parent asset (like BTC or SPX) as a market-wide sentiment filter.

Or combine all of these approaches. But the essence is, using the Flux Composer, to create a consensus view of the market that is more robust than any single signal.

Flux Composer Guide Learn to aggregate signals and build a consensus view of the market.

Order Orchestrator Guide See how the Orchestrator integrates multiple signal sources for execution.

Strategy Templates That Work

These are not rigid rules, but archetypes to guide your thinking.

Conservative Trend Following
Balanced Mean Reversion

Philosophy: Only trade the highest probability setups in clear trends.

Threshold: High, demanding strong signal conviction.
Regime Filter: Only take trades that align with the Market Regime Detector’s trend assessment.
Risk: Lower (1-2% per trade).
Goal: High Reward-to-Risk ratio, lower win rate.

Philosophy: Capture oscillations in ranging or consolidating markets.

Threshold: Lower.
Regime Filter: Primarily trade during in Cyclic (“raning”) market states.
Risk: Lower (0.5-1% per trade) due to higher frequency.
Goal: Higher win rate, lower Reward-to-Risk ratio.

Parameter & Performance Optimisation

How to Adjust Signal Frequency

Too many signals from Flux Composer? This creates noise and overtrading.
Increase the Signal Threshold in Flux Composer (e.g., from 0.5 to 0.7). This demands a stronger consensus from your signal providers.
Decrease Sensitivity or Adaptability in the Market Regime Detector to filter out short-term noise.
Too few signals? You might be missing opportunities.
Decrease the Signal Threshold (e.g., from 0.8 to 0.5).
Check your filters. Ensure the Market Regime Detector or other filters aren’t overly restrictive for the current market conditions. Always adjust in small increments (±0.1) and test the impact over a meaningful period.

How to Tune Parameters Without Overfitting

Overfitting is the cardinal sin of strategy development. It means tuning your strategy to perfectly match the past, rendering it useless for the future. Golden Rules:

Change ONE parameter at a time. If you change two things, you don’t know which one was responsible for the outcome. Test across different market conditions. Your parameters should be robust enough to handle trends, ranges, and volatility. Prefer robust ranges over precise values. If your strategy only works with a lookback of 21 but fails at 20 or 22, it’s overfitted. A good parameter is robust (e.g., works well in the 20-30 range). Forward-test. After finding good parameters on historical data, paper trade them in the live market to see if they hold up.

Pre-Go-Live Testing Checklist

Before risking real capital, ensure your strategy passes this checklist:

Sufficient Backtest: Tested over a long period (ideally 1-2+ years) that includes bull, bear, and sideways markets.
Costs Included: Backtest accounts for commissions and estimated slippage.
Paper Traded: Minimum of one month of live paper trading to confirm real-world behaviour.
Parameters Documented: You have a written record of your exact parameters and the logic behind them.
Drawdown Acceptable: The maximum historical drawdown is within your personal risk tolerance.

Risk Management2

How to Set a Dynamic Stop Loss

A static 2% stop loss doesn’t respect market volatility. Use dynamic methods instead. ATR-Based Stops: The most common method. Place your stop at a multiple of the Average True Range (ATR). Trending Market: Use a wider multiple (e.g., 2-3x ATR) to give the trade room to breathe. Ranging Market: Use a tighter multiple (e.g., 1-1.5x ATR) as moves are expected to be smaller. SuperTrend (Pro): Use the SuperTrend indicator line as a dynamic trailing stop loss.

Order Orchestrator Guide Learn how to implement ATR and SuperTrend-based stops for automated risk management.

How to Use Dynamic Position Sizing

Not all signals are created equal. Your position size should reflect your conviction. A Simple Formula: Position Size = Base Risk % × Signal Strength Factor × Market Regime Factor

Example:

Your base risk is 2%. A strong signal from Flux Composer gives a strength factor of 1.2. The market is ranging, giving a regime factor of 0.8 (you want to risk less). Final Position Size: 2% × 1.2 × 0.8 = 1.92% This allows you to press your edge when conviction is high and pull back when conditions are uncertain.

How to Manage Correlated Positions

Taking two long positions on EUR/USD and GBP/USD is not two separate trades; it’s one big bet on US Dollar weakness. Rules of Thumb:

Know Correlations: Be aware of highly correlated assets (e.g., BTC and ETH, AUD/USD and NZD/USD). Aggregate Exposure: Treat highly correlated trades as a single position from a risk perspective. Diversify: Actively seek positions in uncorrelated assets to diversify your risk.

Risk Management

Dynamic position sizing formula

Simple formula:

Position Size = Base% × Signal Strength × Market Regime

Example:
- Base: 2%
- Signal: 0.8 (strong)
- Regime: 1.2 (trending)
- Final: 2% × 0.8 × 1.2 = 1.92%

Quick rules:

Strong signal + Trending = Full size
Weak signal + Ranging = Half size
Never exceed base size by >50%

Stop loss guidelines

By market regime:

Trending: 2-3 × ATR (wider)
Ranging: 1-1.5 × ATR (tighter)

By signal strength:

Strong (>0.7): Give more room
Medium (0.4-0.7): Standard stops
Weak (<0.4): Tight stops or skip

Universal rule: Never risk >2% per trade

Multiple positions management

Correlation limits:

Same asset: Max 5% total
Correlated pairs: Treat as one position
Same sector: Max 20% total

Example: EUR/USD + GBP/USD = one position (highly correlated)

Priority order:

Strongest signal first
Least correlated second
Different timeframes third

Optimisation Best Practices

Parameter optimisation without overfitting

Golden rules:

Change ONE parameter at a time
Test for minimum 2 weeks per change
Document what works/doesn’t
Prefer robust ranges over precise values

Optimisation order:

Signal threshold first (biggest impact)
Lookback period second
Regime settings third
Fine-tuning last

Overfitting signs:

Parameters too specific (0.47 vs 0.4-0.5)
Only works in backtests
Degrades with small changes
Too many conditions

Strategy templates that work

Conservative Trend Following:

Threshold: 0.5+ (selective)
Only trade trending regimes
Risk: 1-2% per trade
Expected: 45-55% win rate, 1:1.5 RR

Balanced Approach:

Threshold: 0.3-0.5
All regimes with adjustment
Risk: 2% per trade
Expected: 50-60% win rate, 1:1.3 RR

Active Mean Reversion:

Threshold: 0.2-0.3
Prefer ranging regimes
Risk: 0.5-1% per trade
Expected: 60-70% win rate, 1:1 RR

Testing checklist

Before going live:

✓ 2+ years backtest data
✓ Includes up/down/sideways markets
✓ Transaction costs included
✓ 1 month paper trading minimum
✓ Documented all parameters
✓ Tested ±10% parameter variations
✓ Max drawdown acceptable

Red flags:

✗ Only works in trending markets
✗ Requires perfect timing
✗ Win rate >80% (too good to be true)
✗ Parameters change frequently

Quick Tips

Common mistakes to avoid

Over-optimizing: Stick to round numbers (0.3, 0.5, not 0.47)
Too many indicators: 3-4 max, quality > quantity
Ignoring regime: Always check Market Regime first
Chasing signals: Wait for your setup, don’t force trades
Not documenting: Track what works for YOUR style

What actually works

Proven approaches:

Simple > Complex (fewer parameters)
Regime-aware strategies outperform
Position sizing matters more than entry
Consistency beats perfection
Default parameters often best starting point

Success pattern: Master one setup completely before adding complexity

Multi-Signal Strategies

Combining multiple signal providers

Order Orchestrator supports up to 5 signals (Pro):

Signal mapping example:

Signal 1: WaveTrend 4D (primary)
Signal 2: Custom RSI divergence
Signal 3: Volume profile breaks
Signal 4: Market structure
Signal 5: Sentiment indicator

Weighting strategies:

Equal weight: Simple average
Performance-based: Track hit rate
Regime-based: Adjust by market state
Confidence-weighted: Signal strength

Best practices:

Start with 2-3 signals max
Ensure low correlation (<0.7)
Test individually first
Document signal logic

Market hours and session filters

Session templates:

Forex majors:

London: 08:00-16:00 UTC
NY: 13:00-21:00 UTC
Overlap: 13:00-16:00 UTC (best liquidity)

Crypto 24/7 optimisation:

High volume: 12:00-20:00 UTC
Low spread: 08:00-16:00 UTC
Avoid: 00:00-04:00 UTC (thin)

Implementation:

Use Order Orchestrator time filters
Adjust position size by session
Tighter stops during thin markets
Monitor spread widening

Correlation management across positions

Correlation matrix approach:

EURUSD ←→ GBPUSD: 0.85 (high)
EURUSD ←→ USDJPY: -0.70 (inverse)
BTCUSDT ←→ ETHUSDT: 0.90 (very high)

Position limits:

Single asset: Max 5% portfolio
Correlated >0.7: Count as one position
Same sector: Max 20% total
Inverse correlation: Can hedge

Dynamic adjustment:

Reduce size for correlated adds
Increase for diversification
Monitor rolling correlation
Alert on concentration risk

Third-Party Integration

PineConnector setup and webhooks

Order Orchestrator → PineConnector:

Enable in Order Orchestrator:
- Toggle “Enable Webhooks”
- Set command format
- Configure position sizing

Webhook URL format:

https://pineconnector.com/webhook/your-license-key

Alert message template:

LicenseID,{{strategy.order.action}},{{ticker}},
risk={{strategy.order.contracts}},SL={{plot("SL")}},
TP={{plot("TP")}},comment={{strategy.order.comment}}

Supported brokers:

MetaTrader 4/5
cTrader
Interactive Brokers (beta)

PineConnector docs →

API integration for automated execution

Native TradingView webhooks:

JSON payload structure:

{
  "action": "{{strategy.order.action}}",
  "symbol": "{{ticker}}",
  "quantity": "{{strategy.order.contracts}}",
  "price": "{{close}}",
  "timestamp": "{{timenow}}",
  "signal_strength": "{{plot('GYTS.Strength')}}",
  "market_regime": "{{plot('GYTS.Regime')}}"
}

Webhook endpoints:

Your server: Handle JSON POST
Zapier/Make: No-code automation
AWS Lambda: Serverless execution
Custom bots: Discord/Telegram

Security:

Use HTTPS only
Implement authentication
Rate limit requests
Validate payloads

Backtesting to live trading checklist

Pre-live validation:

✓ 2+ years backtest data
✓ Include commission/slippage
✓ Monte Carlo validation (YaeBot)
✓ Paper trade 1 month minimum
✓ Document all parameters
✓ Test webhook connectivity
✓ Verify broker compatibility
✓ Set risk limits

Phased approach:

Week 1-2: Minimum position size
Week 3-4: 50% target size
Week 5+: Full size if profitable

Monitoring:

Daily P&L vs backtest
Slippage tracking
Fill quality metrics
System uptime logs

Advanced Optimisation

Walk-forward analysis methodology

Process:

In-sample: Optimize on 70% data
Out-sample: Test on remaining 30%
Roll forward: Repeat monthly

Parameter stability test:

Run optimisation 10 times
Different data windows
Parameters should be similar
Wide variance = overfitting

Robustness metrics:

Profit factor >1.5 all periods
Max drawdown <2x average
Win rate variance <10%
Sharpe ratio >1.0

Multi-market portfolio approach

Diversification template:

Crypto: 30% (BTCUSDT, ETHUSDT)
Forex: 40% (EURUSD, GBPJPY)
Commodities: 20% (XAUUSD, CL)
Indices: 10% (SPX, NDX)

Correlation monitoring:

Update weekly
Rebalance monthly
Risk parity weighting
Volatility scaling

GYTS implementation:

Run separate instances per market
Adjust parameters by volatility
Share regime detection
Aggregate position sizing

Emergency procedures and failsafes

Circuit breakers:

Daily loss limit: -5% portfolio
Consecutive losses: 5 trades
Drawdown threshold: -15%
Correlation spike: All >0.9

Automation failures:

Primary: TradingView alerts
Backup: Direct API calls
Manual: Pre-set orders
Emergency: Flatten all

Recovery protocol:

Stop all systems
Audit recent trades
Check for data issues
Reduce size on restart
Document lessons learned

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