Trendline Breaks with Multi Fibonacci Supertrend Strategy

24 Mac

TMFS Strategy: Advanced Trendline Breakouts with Multi-Fibonacci Supertrend

Elevate your algorithmic trading with institutional-grade signal confluence


Strategy Genesis & Evolution

This advanced trading system represents the culmination of a personal research journey, evolving from my custom "Multi Fibonacci Supertrend with Signals" indicator into a comprehensive trading strategy. Built upon the exceptional trendline detection methodology pioneered by LuxAlgo in their "Trendlines with Breaks" indicator, I've engineered a systematic framework that integrates multiple technical factors into a cohesive trading system.


Core Fibonacci Principles

At the heart of this strategy lies the Fibonacci sequence application to volatility measurement:

// Fibonacci-based factors for multiple Supertrend calculations
factor1 = input.float(0.618, 'Factor 1 (Weak/Fibonacci)', minval = 0.01, step = 0.01)
factor2 = input.float(1.618, 'Factor 2 (Medium/Golden Ratio)', minval = 0.01, step = 0.01)
factor3 = input.float(2.618, 'Factor 3 (Strong/Extended Fib)', minval = 0.01, step = 0.01)

These precise Fibonacci ratios create a dynamic volatility envelope that adapts to changing market conditions while maintaining mathematical harmony with natural price movements.

Dynamic Trendline Detection

The strategy incorporates LuxAlgo's pioneering approach to trendline detection:

// Pivotal swing detection (inspired by LuxAlgo)
pivot_high = ta.pivothigh(swing_length, swing_length)
pivot_low = ta.pivotlow(swing_length, swing_length)

// Dynamic slope calculation using ATR
slope = atr_value / swing_length * atr_multiplier

// Update trendlines based on pivot detection
if bool(pivot_high)
    upper_slope := slope
    upper_trendline := pivot_high
else
    upper_trendline := nz(upper_trendline) - nz(upper_slope)

This adaptive trendline approach automatically identifies key structural market boundaries, adjusting in real-time to evolving chart patterns.

Breakout State Management

The strategy implements sophisticated state tracking for breakout detection:

// Track breakouts with state variables
var int upper_breakout_state = 0
var int lower_breakout_state = 0

// Update breakout state when price crosses trendlines
upper_breakout_state := bool(pivot_high) ? 0 : close > upper_trendline ? 1 : upper_breakout_state
lower_breakout_state := bool(pivot_low) ? 0 : close < lower_trendline ? 1 : lower_breakout_state

// Detect new breakouts (state transitions)
bool new_upper_breakout = upper_breakout_state > upper_breakout_state[1]
bool new_lower_breakout = lower_breakout_state > lower_breakout_state[1]

This state-based approach enables precise identification of the exact moment when price breaks through a significant trendline.

Multi-Factor Signal Confluence

Entry signals require confirmation from multiple technical factors:

// Define entry conditions with multi-factor confluence
long_entry_condition = enable_long_positions and 
                     upper_breakout_state > upper_breakout_state[1] and  // New trendline breakout
                     di_plus > di_minus and  // Bullish DMI confirmation
                     close > smoothed_trend  // Price above Supertrend envelope

// Execute trades only with full confirmation
if long_entry_condition
    strategy.entry('L', strategy.long, comment = "LONG")

This strict requirement for confluence significantly reduces false signals and improves the quality of trade entries.

Advanced Risk Management

The strategy includes sophisticated risk controls with multiple methodologies:

// Calculate stop loss based on selected method
get_long_stop_loss_price(base_price) =>
    switch stop_loss_method
        'PERC' => base_price * (1 - long_stop_loss_percent)
        'ATR' => base_price - long_stop_loss_atr_multiplier * entry_atr
        'RR' => base_price - (get_long_take_profit_price() - base_price) / long_risk_reward_ratio
        => na

// Implement trailing functionality
strategy.exit(
    id = 'Long Take Profit / Stop Loss', 
    from_entry = 'L', 
    qty_percent = take_profit_quantity_percent, 
    limit = trailing_take_profit_enabled ? na : long_take_profit_price, 
    stop = long_stop_loss_price, 
    trail_price = trailing_take_profit_enabled ? long_take_profit_price : na, 
    trail_offset = trailing_take_profit_enabled ? long_trailing_tp_step_ticks : na, 
    comment = "TP/SL Triggered"
)

This flexible approach adapts to varying market conditions while providing comprehensive downside protection.

Performance Characteristics

Rigorous backtesting demonstrates exceptional capital appreciation potential with impressive risk-adjusted metrics:

• Remarkable total return profile (1,517%+)
  
• Strong Sortino ratio (3.691) indicating superior downside risk control
  
• Profit factor of 1.924 across all trades (2.153 for long positions)
  
• Win rate exceeding 35% with balanced distribution across varied market conditions
  

Institutional Considerations

The strategy architecture addresses execution complexities faced by institutional participants with temporal filtering and date-range capabilities:

// Time Filter settings with flexible timezone support
import jason5480/time_filters/5 as time_filter

src_timezone = input.string(defval = 'Exchange', title = 'Source Timezone')
dst_timezone = input.string(defval = 'Exchange', title = 'Destination Timezone')

// Date range filtering for precise execution windows
use_from_date = input.bool(defval = true, title = 'Enable Start Date')
from_date = input.time(defval = timestamp('01 Jan 2022 00:00'), title = 'Start Date')

// Validate trading permission based on temporal constraints
date_filter_approved = time_filter.is_in_date_range(
    use_from_date, from_date, use_to_date, to_date, src_timezone, dst_timezone
)

These capabilities enable precise execution timing and market session optimization critical for larger market participants.

Acknowledgments

Special thanks to LuxAlgo for the pioneering work on trendline detection and breakout identification that inspired elements of this strategy. Their innovative approach to technical analysis provided a valuable foundation upon which I could build my Fibonacci-based methodology.

This strategy is shared under the same Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license as LuxAlgo's original work.

Past performance is not indicative of future results. Conduct thorough analysis before implementing any algorithmic strategy.

4 Apr

Nota Keluaran

Addressing Moderation Feedback: Enhanced Clarity and Realism

This addendum clarifies key aspects of the TMFS Strategy in response to TradingView Moderation feedback, focusing on demonstrating originality beyond a simple indicator combination and providing context for realistic strategy application.

Originality: Synergistic Integration, Not Just Combination

TMFS's originality lies in its *synergistic integration* of components, not just their aggregation. Key aspects include:

• Harmonic Volatility Context: Specific Fibonacci ratios (0.618, 1.618, 2.618) are intentionally used in the multi-level Supertrend, aiming for a volatility analysis potentially more attuned to market harmonics. The averaged and smoothed result creates a *unique dynamic filter* for breakouts.
  
• Multi-Stage Confluence Validation: The core innovation is the *strict three-tiered validation*: a Trendline Break *must* be confirmed by both the price's interaction with the Fibonacci Supertrend dynamic zone *and* by DMI momentum. This engineered confluence represents a distinct methodology designed to significantly enhance signal reliability over simpler approaches.
  

This purpose-built system prioritizes higher signal quality through rigorous, multi-factor validation.

Realistic Strategy Settings and Performance Context

To align with responsible trading practices and moderation guidelines, the strategy defaults have been updated:

strategy('TMFS Strategy', ..., initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=4, commission_value=0.03, slippage=2, pyramiding=2) // Key Defaults

• Realistic Defaults: Settings now reflect more practical parameters: $10k initial capital, 0.03% commission, 2 ticks slippage. The default risk is set at 4% equity per initial trade (`default_qty_value=4`), a level requiring careful user consideration and adjustment based on individual risk tolerance.
  
• Historical Snapshot (Example): Backtesting with these defaults ([Specify Asset/Timeframe/Date Range]) showed an overall Profit Factor of 1.376 and a Max Drawdown of 1.16%. Long performance (PF 1.616) historically outperformed short performance (PF 0.965). Other metrics like Sortino (0.262) and Sharpe (0.129) reflect the specific test period. These are historical metrics, not future guarantees.
• Risk Management is Crucial: Past results don't predict future outcomes. Market dynamics change. The included risk management tools (stops, targets, trails) *must* be actively used. The `pyramiding = 2` setting increases potential but also risk, demanding careful management.
  

The focus is on providing a robust analytical tool; profitability depends on numerous factors including user implementation and market conditions.

Updated Disclaimer Addendum: Risk and Responsibility

Trading involves substantial risk. TMFS is an analytical tool, not a guaranteed profit system. Past performance is not indicative of future results. Users are solely responsible for their decisions. Thorough testing and risk management are essential. Deploy at your own considered risk.