Memecoins — Indikatoren und Strategien

Copeland Dynamic Dominance Matrix System | GForge Copeland Dynamic Dominance Matrix System | GForge - v1 --- 📊 COMPREHENSIVE SYSTEM OVERVIEW The GForge Dynamic BB% TrendSync System represents a revolutionary approach to algorithmic portfolio management, combining cutting-edge statistical analysis, momentum detection, and regime identification into a unified framework. This system processes up to 39 different cryptocurrency assets simultaneously, using advanced mathematical models to determine optimal capital allocation across dynamic market conditions. Core Innovation: Multi-Dimensional Analysis Unlike traditional single-asset indicators, this system operates on multiple analytical dimensions: Momentum Analysis: Dual Bollinger Band Modified Deviation (DBBMD) calculations Relative Strength: Comprehensive dominance matrix with head-to-head comparisons Fundamental Screening: Alpha and Beta statistical filtering Market Regime Detection: Five-component statistical testing framework Portfolio Optimization: Dynamic weighting and allocation algorithms Risk Management: Multi-layered protection and regime-based positioning --- 🔧 DETAILED COMPONENT BREAKDOWN 1. Dynamic Bollinger Band % Modified Deviation Engine (DBBMD) The foundation of this system is an advanced oscillator that combines two independent Bollinger Band systems with asymmetric parameters to create unique momentum readings. Technical Implementation: [ // BB System 1: Fast-reacting with extended standard deviation primary_bb1_ma_len = 40 // Shorter MA for responsiveness primary_bb1_sd_len = 65 // Longer SD for stability primary_bb1_mult = 1.0 // Standard deviation multiplier // BB System 2: Complementary asymmetric design primary_bb2_ma_len = 8 // Longer MA for trend following primary_bb2_sd_len = 66 // Shorter SD for volatility sensitivity primary_bb2_mult = 1.7 // Wider bands for reduced noise Key Features: Asymmetric Design: The intentional mismatch between MA and Standard Deviation periods creates unique oscillation characteristics that traditional Bollinger Bands cannot achieve Percentage Scale: All readings are normalized to 0-100% scale for consistent interpretation across assets Multiple Combination Modes: BB1 Only: Fast/reactive system BB2 Only: Smooth/stable system Average: Balanced blend (recommended) Both Required: Conservative (both must agree) Either One: Aggressive (either can trigger) Mean Deviation Filter: Additional volatility-based layer that measures the standard deviation of the DBBMD% itself, creating dynamic trigger bands Signal Generation Logic: // Primary thresholds primary_long_threshold = 71 // DBBMD% level for bullish signals primary_short_threshold = 33 // DBBMD% level for bearish signals // Mean Deviation creates dynamic bands around these thresholds upper_md_band = combined_bb + (md_mult * bb_std) lower_md_band = combined_bb - (md_mult * bb_std) // Signal triggers when DBBMD crosses these dynamic bands long_signal = lower_md_band > long_threshold short_signal = upper_md_band < short_threshold For more information on this BB% indicator, find it here: 2. Revolutionary Dominance Matrix System This is the system's most sophisticated innovation - a comprehensive framework that compares every asset against every other asset to determine relative strength hierarchies. Mathematical Foundation: The system constructs a mathematical matrix where each cell represents whether asset i dominates asset j: // Core dominance matrix (39x39 for maximum assets) var matrix dominance_matrix = matrix.new(39, 39, 0) // For each qualifying asset pair (i,j): for i = 0 to active_count - 1 for j = 0 to active_count - 1 if i != j // Calculate price ratio BB% TrendSync for asset_i/asset_j ratio_array = calculate_price_ratios(asset_i, asset_j) ratio_dbbmd = calculate_dbbmd(ratio_array) // Asset i dominates j if ratio is in uptrend if ratio_dbbmd_state == 1 matrix.set(dominance_matrix, i, j, 1) Copeland Scoring Algorithm: Each asset receives a dominance score calculated as: Dominance Score = Total Wins - Total Losses // Calculate net dominance for each asset for i = 0 to active_count - 1 wins = 0 losses = 0 for j = 0 to active_count - 1 if i != j if matrix.get(dominance_matrix, i, j) == 1 wins += 1 else losses += 1 copeland_score = wins - losses array.set(dominance_scores, i, copeland_score) Head-to-Head Analysis Process: Ratio Construction: For each asset pair, calculate price_asset_A / price_asset_B DBBMD Application: Apply the same DBBMD analysis to these ratios Trend Determination: If ratio DBBMD shows uptrend, Asset A dominates Asset B Matrix Population: Store dominance relationships in mathematical matrix Score Calculation: Sum wins minus losses for final ranking This creates a tournament-style ranking where each asset's strength is measured against all others, not just against a benchmark. 3. Advanced Alpha & Beta Filtering System The system incorporates fundamental analysis through Capital Asset Pricing Model (CAPM) calculations to filter assets based on risk-adjusted performance. Alpha Calculation (Excess Return Analysis): // CAPM Alpha calculation f_calc_alpha(asset_prices, benchmark_prices, alpha_length, beta_length, risk_free_rate) => // Calculate asset and benchmark returns asset_returns = calculate_returns(asset_prices, alpha_length) benchmark_returns = calculate_returns(benchmark_prices, alpha_length) // Get beta for expected return calculation beta = f_calc_beta(asset_prices, benchmark_prices, beta_length) // Average returns over period avg_asset_return = array_average(asset_returns) * 100 avg_benchmark_return = array_average(benchmark_returns) * 100 // Expected return using CAPM: E(R) = Beta * Market_Return + Risk_Free_Rate expected_return = beta * avg_benchmark_return + risk_free_rate // Alpha = Actual Return - Expected Return alpha = avg_asset_return - expected_return Beta Calculation (Volatility Relationship): // Beta measures how much an asset moves relative to benchmark f_calc_beta(asset_prices, benchmark_prices, length) => // Calculate return series for both assets asset_returns = benchmark_returns = // Populate return arrays for i = 0 to length - 1 asset_return = (current_price - previous_price) / previous_price benchmark_return = (current_bench - previous_bench) / previous_bench // Calculate covariance and variance covariance = calculate_covariance(asset_returns, benchmark_returns) benchmark_variance = calculate_variance(benchmark_returns) // Beta = Covariance(Asset, Market) / Variance(Market) beta = covariance / benchmark_variance Filtering Applications: Alpha Filter: Only includes assets with alpha above specified threshold (e.g., >0.5% monthly excess return) Beta Filter: Screens for desired volatility characteristics (e.g., beta >1.0 for aggressive assets) Combined Screening: Both filters must pass for asset qualification Dynamic Thresholds: User-configurable parameters for different market conditions 4. Intelligent Tie-Breaking Resolution System When multiple assets have identical dominance scores, the system employs sophisticated methods to determine final rankings. Standard Tie-Breaking Hierarchy: // Primary tie-breaking logic if score_i == score_j // Tied dominance scores // Level 1: Compare Beta values (higher beta wins) beta_i = array.get(beta_values, i) beta_j = array.get(beta_values, j) if beta_j > beta_i swap_positions(i, j) else if beta_j == beta_i // Level 2: Compare Alpha values (higher alpha wins) alpha_i = array.get(alpha_values, i) alpha_j = array.get(alpha_values, j) if alpha_j > alpha_i swap_positions(i, j) Advanced Tie-Breaking (Head-to-Head Analysis): For the top 3 performers, an enhanced tie-breaking mechanism analyzes direct head-to-head price ratio performance: // Advanced tie-breaker for top performers f_advanced_tiebreaker(asset1_idx, asset2_idx, lookback_period) => // Calculate price ratio over lookback period ratio_history = for k = 0 to lookback_period - 1 price_ratio = price_asset1 / price_asset2 array.push(ratio_history, price_ratio) // Apply simplified trend analysis to ratio current_ratio = array.get(ratio_history, 0) average_ratio = calculate_average(ratio_history) // Asset 1 wins if current ratio > average (trending up) if current_ratio > average_ratio return 1 // Asset 1 dominates else return -1 // Asset 2 dominates 5. Five-Component Aggregate Market Regime Filter This sophisticated framework combines multiple statistical tests to determine whether market conditions favor trending strategies or require defensive positioning. Component 1: Augmented Dickey-Fuller (ADF) Test Tests for unit root presence to distinguish between trending and mean-reverting price series. // Simplified ADF implementation calculate_adf_statistic(price_series, lookback) => // Calculate first differences differences = for i = 0 to lookback - 2 diff = price_series - price_series array.push(differences, diff) // Statistical analysis of differences mean_diff = calculate_mean(differences) std_diff = calculate_standard_deviation(differences) // ADF statistic approximation adf_stat = mean_diff / std_diff // Compare against threshold for trend determination is_trending = adf_stat <= adf_threshold Component 2: Directional Movement Index (DMI) Classic Wilder indicator measuring trend strength through directional movement analysis. // DMI calculation for trend strength calculate_dmi_signal(high_data, low_data, close_data, period) => // Calculate directional movements plus_dm_sum = 0.0 minus_dm_sum = 0.0 true_range_sum = 0.0 for i = 1 to period // Directional movements up_move = high_data - high_data down_move = low_data - low_data // Accumulate positive/negative movements if up_move > down_move and up_move > 0 plus_dm_sum += up_move if down_move > up_move and down_move > 0 minus_dm_sum += down_move // True range calculation true_range_sum += calculate_true_range(i) // Calculate directional indicators di_plus = 100 * plus_dm_sum / true_range_sum di_minus = 100 * minus_dm_sum / true_range_sum // ADX calculation dx = 100 * math.abs(di_plus - di_minus) / (di_plus + di_minus) adx = dx // Simplified for demonstration // Trending if ADX above threshold is_trending = adx > dmi_threshold Component 3: KPSS Stationarity Test Complementary test to ADF that examines stationarity around trend components. // KPSS test implementation calculate_kpss_statistic(price_series, lookback, significance_level) => // Calculate mean and variance series_mean = calculate_mean(price_series, lookback) series_variance = calculate_variance(price_series, lookback) // Cumulative sum of deviations cumulative_sum = 0.0 cumsum_squared_sum = 0.0 for i = 0 to lookback - 1 deviation = price_series - series_mean cumulative_sum += deviation cumsum_squared_sum += math.pow(cumulative_sum, 2) // KPSS statistic kpss_stat = cumsum_squared_sum / (lookback * lookback * series_variance) // Compare against critical values critical_value = significance_level == 0.01 ? 0.739 : significance_level == 0.05 ? 0.463 : 0.347 is_trending = kpss_stat >= critical_value Component 4: Choppiness Index Measures market directionality using fractal dimension analysis of price movement. // Choppiness Index calculation calculate_choppiness(price_data, period) => // Find highest and lowest over period highest = price_data lowest = price_data true_range_sum = 0.0 for i = 0 to period - 1 if price_data > highest highest := price_data if price_data < lowest lowest := price_data // Accumulate true range if i > 0 true_range = calculate_true_range(price_data, i) true_range_sum += true_range // Choppiness calculation range_high_low = highest - lowest choppiness = 100 * math.log10(true_range_sum / range_high_low) / math.log10(period) // Trending if choppiness below threshold (typically 61.8) is_trending = choppiness < 61.8 Component 5: Hilbert Transform Analysis Phase-based cycle detection and trend identification using mathematical signal processing. // Hilbert Transform trend detection calculate_hilbert_signal(price_data, smoothing_period, filter_period) => // Smooth the price data smoothed_price = calculate_moving_average(price_data, smoothing_period) // Calculate instantaneous phase components // Simplified implementation for demonstration instant_phase = smoothed_price delayed_phase = calculate_moving_average(price_data, filter_period) // Compare instantaneous vs delayed signals phase_difference = instant_phase - delayed_phase // Trending if instantaneous leads delayed is_trending = phase_difference > 0 Aggregate Regime Determination: // Combine all five components regime_calculation() => trending_count = 0 total_components = 0 // Test each enabled component if enable_adf and adf_signal == 1 trending_count += 1 if enable_adf total_components += 1 // Repeat for all five components... // Calculate trending proportion trending_proportion = trending_count / total_components // Market is trending if proportion above threshold regime_allows_trading = trending_proportion >= regime_threshold The system only allows asset positions when the specified percentage of components indicate trending conditions. During choppy or mean-reverting periods, the system automatically positions in USD to preserve capital. 6. Dynamic Portfolio Weighting Framework Six sophisticated allocation methodologies provide flexibility for different market conditions and risk preferences. Weighting Method Implementations: 1. Equal Weight Distribution: // Simple equal allocation if weighting_mode == "Equal Weight" weight_per_asset = 1.0 / selection_count for i = 0 to selection_count - 1 array.push(weights, weight_per_asset) 2. Linear Dominance Scaling: // Linear scaling based on dominance scores if weighting_mode == "Linear Dominance" // Normalize scores to 0-1 range min_score = array.min(dominance_scores) max_score = array.max(dominance_scores) score_range = max_score - min_score total_weight = 0.0 for i = 0 to selection_count - 1 score = array.get(dominance_scores, i) normalized = (score - min_score) / score_range weight = 1.0 + normalized * concentration_factor array.push(weights, weight) total_weight += weight // Normalize to sum to 1.0 for i = 0 to selection_count - 1 current_weight = array.get(weights, i) array.set(weights, i, current_weight / total_weight) 3. Conviction Score (Exponential): // Exponential scaling for high conviction if weighting_mode == "Conviction Score" // Combine dominance score with DBBMD strength conviction_scores = for i = 0 to selection_count - 1 dominance = array.get(dominance_scores, i) dbbmd_strength = array.get(dbbmd_values, i) conviction = dominance + (dbbmd_strength - 50) / 25 array.push(conviction_scores, conviction) // Exponential weighting total_weight = 0.0 for i = 0 to selection_count - 1 conviction = array.get(conviction_scores, i) normalized = normalize_score(conviction) weight = math.pow(1 + normalized, concentration_factor) array.push(weights, weight) total_weight += weight // Final normalization normalize_weights(weights, total_weight) Advanced Features: Minimum Position Constraint: Prevents dust allocations below specified threshold Concentration Factor: Adjustable parameter controlling weight distribution aggressiveness Dominance Boost: Extra weight for assets exceeding specified dominance thresholds Dynamic Rebalancing: Automatic weight recalculation on portfolio changes 7. Intelligent USD Management System The system treats USD as a competing asset with its own dominance score, enabling sophisticated cash management. USD Scoring Methodologies: Smart Competition Mode (Recommended): f_calculate_smart_usd_dominance() => usd_wins = 0 // USD beats assets in downtrends or weak uptrends for i = 0 to active_count - 1 asset_state = get_asset_state(i) asset_dbbmd = get_asset_dbbmd(i) // USD dominates shorts and weak longs if asset_state == -1 or (asset_state == 1 and asset_dbbmd < long_threshold) usd_wins += 1 // Calculate Copeland-style score base_score = usd_wins - (active_count - usd_wins) // Boost during weak market conditions qualified_assets = count_qualified_long_assets() if qualified_assets <= active_count * 0.2 base_score := math.round(base_score * usd_boost_factor) base_score Auto Short Count Mode: // USD dominance based on number of bearish assets usd_dominance = count_assets_in_short_state() // Apply boost during low activity if qualified_long_count <= active_count * 0.2 usd_dominance := usd_dominance * usd_boost_factor Regime-Based USD Positioning: When the five-component regime filter indicates unfavorable conditions, the system automatically overrides all asset signals and positions 100% in USD, protecting capital during choppy markets. 8. Multi-Asset Infrastructure & Data Management The system maintains comprehensive data structures for up to 39 assets simultaneously. Data Collection Framework: // Full OHLC data matrices (200 bars depth for performance) var matrix open_data = matrix.new(39, 200, na) var matrix high_data = matrix.new(39, 200, na) var matrix low_data = matrix.new(39, 200, na) var matrix close_data = matrix.new(39, 200, na) // Real-time data collection if barstate.isconfirmed for i = 0 to active_count - 1 ticker = array.get(assets, i) = request.security(ticker, timeframe.period, [open , high , low , close ], lookahead=barmerge.lookahead_off) // Store in matrices with proper shifting matrix.set(open_data, i, 0, nz(o, 0)) matrix.set(high_data, i, 0, nz(h, 0)) matrix.set(low_data, i, 0, nz(l, 0)) matrix.set(close_data, i, 0, nz(c, 0)) Asset Configuration: The system comes pre-configured with 39 major cryptocurrency pairs across multiple exchanges: Major Pairs: BTC, ETH, XRP, SOL, DOGE, ADA, etc. Exchange Coverage: Binance, KuCoin, MEXC for optimal liquidity Configurable Count: Users can activate 2-39 assets based on preferences Custom Tickers: All asset selections are user-modifiable --- ⚙️ COMPREHENSIVE CONFIGURATION GUIDE Portfolio Management Settings Maximum Portfolio Size (1-10): Conservative (1-2): High concentration, captures strong trends Balanced (3-5): Moderate diversification with trend focus Diversified (6-10): Lower concentration, broader market exposure Dominance Clarity Threshold (0.1-1.0): Low (0.1-0.4): Prefers diversification, holds multiple assets frequently Medium (0.5-0.7): Balanced approach, context-dependent allocation High (0.8-1.0): Concentration-focused, single asset preference Signal Generation Parameters DBBMD Thresholds: // Standard configuration primary_long_threshold = 71 // Conservative: 75+, Aggressive: 65-70 primary_short_threshold = 33 // Conservative: 25-30, Aggressive: 35-40 // BB System parameters bb1_ma_len = 40 // Fast system: 20-50 bb1_sd_len = 65 // Stability: 50-80 bb2_ma_len = 8 // Trend: 60-100 bb2_sd_len = 66 // Sensitivity: 10-20 Risk Management Configuration Alpha/Beta Filters: Alpha Threshold: 0.0-2.0% (higher = more selective) Beta Threshold: 0.5-2.0 (1.0+ for aggressive assets) Calculation Periods: 20-50 bars (longer = more stable) Regime Filter Settings: Trending Threshold: 0.3-0.8 (higher = stricter trend requirements) Component Lookbacks: 30-100 bars (balance responsiveness vs stability) Enable/Disable: Individual component control for customization --- 📊 PERFORMANCE TRACKING & VISUALIZATION Real-Time Dashboard Features The compact dashboard provides essential information: Current Holdings: Asset names and allocation percentages Dominance Score: Current position's relative strength ranking Active Assets: Qualified long signals vs total asset count Returns: Total portfolio performance percentage Maximum Drawdown: Peak-to-trough decline measurement Trade Count: Total portfolio transitions executed Regime Status: Current market condition assessment Comprehensive Ranking Table The left-side table displays detailed asset analysis: Ranking Position: Numerical order by dominance score Asset Symbol: Clean ticker identification with color coding Dominance Score: Net wins minus losses in head-to-head comparisons Win-Loss Record: Detailed breakdown of dominance relationships DBBMD Reading: Current momentum percentage with threshold highlighting Alpha/Beta Values: Fundamental analysis metrics when filters enabled Portfolio Weight: Current allocation percentage in signal portfolio Execution Status: Visual indicator of actual holdings vs signals Visual Enhancement Features Color-Coded Assets: 39 distinct colors for easy identification Regime Background: Red tinting during unfavorable market conditions Dynamic Equity Curve: Portfolio value plotted with position-based coloring Status Indicators: Symbols showing execution vs signal states --- 🔍 ADVANCED TECHNICAL FEATURES State Persistence System The system maintains asset states across bars to prevent excessive switching: // State tracking for each asset and ratio combination var array asset_states = array.new(1560, 0) // 39 * 40 ratios // State changes only occur on confirmed threshold breaks if long_crossover and current_state != 1 current_state := 1 array.set(asset_states, asset_index, 1) else if short_crossover and current_state != -1 current_state := -1 array.set(asset_states, asset_index, -1) Transaction Cost Integration Realistic modeling of trading expenses: // Transaction cost calculation transaction_fee = 0.4 // Default 0.4% (fees + slippage) // Applied on portfolio transitions if should_execute_transition was_holding_assets = check_current_holdings() will_hold_assets = check_new_signals() // Charge fees for meaningful transitions if transaction_fee > 0 and (was_holding_assets or will_hold_assets) fee_amount = equity * (transaction_fee / 100) equity -= fee_amount total_fees += fee_amount Dynamic Memory Management Optimized data structures for performance: 200-Bar History: Sufficient for calculations while maintaining speed Matrix Operations: Efficient storage and retrieval of multi-asset data Array Recycling: Memory-conscious data handling for long-running backtests Conditional Calculations: Skip unnecessary computations during initialization 12H 30 assets portfolio --- 🚨 SYSTEM LIMITATIONS & TESTING STATUS CURRENT DEVELOPMENT PHASE: ACTIVE TESTING & OPTIMIZATION This system represents cutting-edge algorithmic trading technology but remains in continuous development. Key considerations: Known Limitations: Requires significant computational resources for 39-asset analysis Performance varies significantly across different market conditions Complex parameter interactions may require extensive optimization Slippage and liquidity constraints not fully modeled for all assets No consideration for market impact in large position sizes Areas Under Active Development: Enhanced regime detection algorithms Improved transaction cost modeling Additional portfolio weighting methodologies Machine learning integration for parameter optimization Cross-timeframe analysis capabilities --- 🔒 ANTI-REPAINTING ARCHITECTURE & LIVE TRADING READINESS One of the most critical aspects of any trading system is ensuring that signals and calculations are based on confirmed, historical data rather than current bar information that can change throughout the trading session. This system implements comprehensive anti-repainting measures to ensure 100% reliability for live trading . The Repainting Problem in Trading Systems Repainting occurs when an indicator uses current, unconfirmed bar data in its calculations, causing: False Historical Signals: Backtests appear better than reality because calculations change as bars develop Live Trading Failures: Signals that looked profitable in testing fail when deployed in real markets Inconsistent Results: Different results when running the same indicator at different times during a trading session Misleading Performance: Inflated win rates and returns that cannot be replicated in practice GForge Anti-Repainting Implementation This system eliminates repainting through multiple technical safeguards: 1. Historical Data Usage for All Calculations // CRITICAL: All calculations use PREVIOUS bar data (note the offset) = request.security(ticker, timeframe.period, [open , high , low , close , close], lookahead=barmerge.lookahead_off) // Store confirmed previous bar OHLC for calculations matrix.set(open_data, i, 0, nz(o1, 0)) // Previous bar open matrix.set(high_data, i, 0, nz(h1, 0)) // Previous bar high matrix.set(low_data, i, 0, nz(l1, 0)) // Previous bar low matrix.set(close_data, i, 0, nz(c1, 0)) // Previous bar close // Current bar close only for visualization matrix.set(current_prices, i, 0, nz(c0, 0)) // Live price display 2. Confirmed Bar State Processing // Only process data when bars are confirmed and closed if barstate.isconfirmed // All signal generation and portfolio decisions occur here // using only historical, unchanging data // Shift historical data arrays for i = 0 to active_count - 1 for bar = math.min(data_bars, 199) to 1 // Move confirmed data through historical matrices old_data = matrix.get(close_data, i, bar - 1) matrix.set(close_data, i, bar, old_data) // Process new confirmed bar data calculate_all_signals_and_dominance() 3. Lookahead Prevention // Explicit lookahead prevention in all security calls request.security(ticker, timeframe.period, expression, lookahead=barmerge.lookahead_off) // This ensures no future data can influence current calculations // Essential for maintaining signal integrity across all timeframes 4. State Persistence with Historical Validation // Asset states only change based on confirmed threshold breaks // using historical data that cannot change var array asset_states = array.new(1560, 0) // State changes use only confirmed, previous bar calculations if barstate.isconfirmed = f_calculate_enhanced_dbbmd(confirmed_price_array, ...) // Only update states after bar confirmation if long_crossover_confirmed and current_state != 1 current_state := 1 array.set(asset_states, asset_index, 1) Live Trading vs. Backtesting Consistency The system's architecture ensures identical behavior in both environments: Backtesting Mode: Uses historical offset data for all calculations Processes confirmed bars with `barstate.isconfirmed` Maintains identical signal generation logic No access to future information Live Trading Mode: Uses same historical offset data structure Waits for bar confirmation before signal updates Identical mathematical calculations and thresholds Real-time price display without affecting signals Technical Implementation Details Data Collection Timing // Example of proper data collection timing if barstate.isconfirmed // Wait for bar to close // Collect PREVIOUS bar's confirmed OHLC data for i = 0 to active_count - 1 ticker = array.get(assets, i) // Get confirmed previous bar data (note offset) = request.security(ticker, timeframe.period, [open , high , low , close , close], lookahead=barmerge.lookahead_off) // ALL calculations use prev_* values // current_close only for real-time display portfolio_calculations_use_previous_bar_data() Signal Generation Process // Signal generation workflow (simplified) if barstate.isconfirmed and data_bars >= minimum_required_bars // Step 1: Calculate DBBMD using historical price arrays for i = 0 to active_count - 1 historical_prices = get_confirmed_price_history(i) // Uses offset data = calculate_dbbmd(historical_prices) update_asset_state(i, state) // Step 2: Build dominance matrix using confirmed data calculate_dominance_relationships() // All historical data // Step 3: Generate portfolio signals new_portfolio = generate_target_portfolio() // Based on confirmed calculations // Step 4: Compare with previous signals for changes if portfolio_signals_changed() execute_portfolio_transition() Verification Methods for Users Users can verify the anti-repainting behavior through several methods: 1. Historical Replay Test Run the indicator on historical data Note signal timing and portfolio changes Replay the same period - signals should be identical No retroactive changes in historical signals 2. Intraday Consistency Check Load indicator during active trading session Observe that previous day's signals remain unchanged Only current day's final bar should show potential signal changes Refresh indicator - historical signals should be identical Live Trading Deployment Considerations Data Quality Assurance Exchange Connectivity: Ensure reliable data feeds for all 39 assets Missing Data Handling: System includes safeguards for data gaps Price Validation: Automatic filtering of obvious price errors Timeframe Synchronization: All assets synchronized to same bar timing Performance Impact of Anti-Repainting Measures The robust anti-repainting implementation requires additional computational resources: Memory Usage: 200-bar historical data storage for 39 assets Processing Delay: Signals update only after bar confirmation Calculation Overhead: Multiple historical data validations Alert Timing: Slight delay compared to current-bar indicators However, these trade-offs are essential for reliable live trading performance and accurate backtesting results. Critical: Equity Curve Anti-Repainting Architecture The most sophisticated aspect of this system's anti-repainting design is the temporal separation between signal generation and performance calculation . This creates a realistic trading simulation that perfectly matches live trading execution. The Timing Sequence // STEP 1: Store what we HELD during the current bar (for performance calc) if barstate.isconfirmed // Record positions that were active during this bar array.clear(held_portfolio) array.clear(held_weights) for i = 0 to array.size(execution_portfolio) - 1 array.push(held_portfolio, array.get(execution_portfolio, i)) array.push(held_weights, array.get(execution_weights, i)) // STEP 2: Calculate performance based on what we HELD portfolio_return = 0.0 for i = 0 to array.size(held_portfolio) - 1 held_asset = array.get(held_portfolio, i) held_weight = array.get(held_weights, i) // Performance from current_price vs reference_price // This is what we ACTUALLY earned during this bar if held_asset != "USD" current_price = get_current_price(held_asset) // End of bar reference_price = get_reference_price(held_asset) // Start of bar asset_return = (current_price - reference_price) / reference_price portfolio_return += asset_return * held_weight // STEP 3: Apply return to equity (realistic timing) equity := equity * (1 + portfolio_return) // STEP 4: Generate NEW signals for NEXT period (using confirmed data) = f_generate_target_portfolio() // STEP 5: Execute transitions if signals changed if signal_changed // Update execution_portfolio for NEXT bar array.clear(execution_portfolio) array.clear(execution_weights) for i = 0 to array.size(new_signal_portfolio) - 1 array.push(execution_portfolio, array.get(new_signal_portfolio, i)) array.push(execution_weights, array.get(new_signal_weights, i)) Why This Prevents Equity Curve Repainting Performance Attribution: Returns are calculated based on positions that were **actually held** during each bar, not future signals Signal Timing: New signals are generated **after** performance calculation, affecting only **future** bars Realistic Execution: Mimics real trading where you earn returns on current positions while planning future moves No Retroactive Changes: Once a bar closes, its performance contribution to equity is permanent and unchangeable The One-Bar Offset Mechanism This system implements a critical one-bar timing offset: // Bar N: Performance Calculation // ================================ // 1. Calculate returns on positions held during Bar N // 2. Update equity based on actual holdings during Bar N // 3. Plot equity point for Bar N (based on what we HELD) // Bar N: Signal Generation // ======================== // 4. Generate signals for Bar N+1 (using confirmed Bar N data) // 5. Send alerts for what will be held during Bar N+1 // 6. Update execution_portfolio for Bar N+1 // Bar N+1: The Cycle Continues // ============================= // 1. Performance calculated on positions from Bar N signals // 2. New signals generated for Bar N+2 Alert System Timing The alert system reflects this sophisticated timing: Transaction Cost Realism Even transaction costs follow realistic timing: // Fees applied when transitioning between different portfolios if should_execute_transition // Charge fees BEFORE taking new positions (realistic timing) if transaction_fee > 0 fee_amount = equity * (transaction_fee / 100) equity -= fee_amount // Immediate cost impact total_fees += fee_amount // THEN update to new portfolio update_execution_portfolio(new_signals) transitions += 1 // Fees reduce equity immediately, affecting all future calculations // This matches real trading where fees are deducted upon execution LIVE TRADING CERTIFICATION: This system has been specifically designed and tested for live trading deployment. The comprehensive anti-repainting measures ensure that: Backtesting results accurately represent real trading potential Signals are generated using only confirmed, historical data No retroactive changes can occur to previously generated signals Portfolio transitions are based on reliable, unchanging calculations Performance metrics reflect realistic trading outcomes including proper timing Users can deploy this system with confidence that live trading results will closely match backtesting performance, subject to normal market execution factors such as slippage and liquidity. --- ⚡ ALERT SYSTEM & AUTOMATION The system provides comprehensive alerting for automation and monitoring: Available Alert Conditions Portfolio Signal Change: Triggered when new portfolio composition is generated Regime Override Active: Alerts when market regime forces USD positioning Individual Asset Signals: Can be configured for specific asset transitions Performance Thresholds: Drawdown or return-based notifications --- 📈 BACKTESTING & PERFORMANCE ANALYSIS 8 Comprehensive Metrics Tracking The system maintains detailed performance statistics: Equity Curve: Real-time portfolio value progression Returns Calculation: Total and annualized performance metrics Drawdown Analysis: Peak-to-trough decline measurements Transaction Counting: Portfolio transition frequency Fee Tracking: Cumulative transaction cost impact Win Rate Analysis: Success rate of position changes Backtesting Configuration // Backtesting parameters initial_capital = 10000.0 // Starting capital use_custom_start = true // Enable specific start date custom_start = timestamp("2023-09-01") // Backtest beginning transaction_fee = 0.4 // Combined fees and slippage % // Performance calculation total_return = (equity - initial_capital) / initial_capital * 100 current_drawdown = (peak_equity - equity) / peak_equity * 100 --- 🔧 TROUBLESHOOTING & OPTIMIZATION Common Configuration Issues Insufficient Data: Ensure 100+ bars available before start date [*} Not Compiling: Go on an asset's price chart with 2 or 3 years of data to make the system compile or just simply reapply the indicator again Too Many Assets: Reduce active count if experiencing timeouts Regime Filter Too Strict: Lower trending threshold if always in USD Excessive Switching: Increase MD multiplier or adjust thresholds --- 💡 USER FEEDBACK & ENHANCEMENT REQUESTS The continuous evolution of this system depends heavily on user experience and community feedback. Your insights will help motivate me for new improvements and new feature developments. --- ⚖️ FINAL COMPREHENSIVE RISK DISCLAIMER TRADING INVOLVES SUBSTANTIAL RISK OF LOSS This indicator is a sophisticated analytical tool designed for educational and research purposes. Important warnings and considerations: System Limitations: No algorithmic system can guarantee profitable outcomes Complex systems may fail in unexpected ways during extreme market events Historical backtesting does not account for all real-world trading challenges Slippage, liquidity constraints, and market impact can significantly affect results System parameters require careful optimization and ongoing monitoring The creator and distributor of this indicator assume no liability for any financial losses, system failures, or adverse outcomes resulting from its use. This tool is provided "as is" without any warranties, express or implied. By using this indicator, you acknowledge that you have read, understood, and agreed to assume all risks associated with algorithmic trading and cryptocurrency investments.

MEMEQUANT MEMEQUANT This script is a comprehensive and specialized tool designed for tracking trends and money flow within meme coins and DEX tokens. By combining various features such as trend lines, Fibonacci levels, and category-based indices, it helps traders make informed decisions in highly volatile markets. Key Features: 1. Category-Based Indices: • Tracks the performance of token categories like: • AI Agent Tokens • AI Tokens • Animal Tokens • Murad Picks • Each category consists of leader tokens, which are selected based on their higher market cap and trading volume. These tokens act as benchmarks for their respective categories. • Visualizes category indices in a line chart to identify trends and compare money flow between categories. 2. Fibonacci Correction Zones: • Highlights key retracement levels (e.g., 60%, 70%, 80%). • These levels are crucial for identifying potential reversal zones, commonly observed in meme coin trading patterns. • Fully customizable to match individual trading strategies. 3. Trend Lines: • Automatically detects major support and resistance levels. • Separates long-term and short-term trend lines, allowing traders to focus on significant price movements. 4. Enhanced Info Table: • Provides real-time insights, including: • % Distance from All-Time High (ATH) • Current Trading Volume • 50-bar Average Volume • Volume Change Percentage • Displays information in an easy-to-read table on the chart. 5. Customizable Settings: • Users can adjust transparency, colors, and ranges for Fibonacci zones, trend lines, and the table. • Enables or disables individual features (e.g., Fibonacci, trend lines, table) based on preferences. How It Works: 1. Tracking Money Flow Across Categories: • The script calculates the market cap to volume ratio for each category of tokens to help identify the dominant trend. • A higher ratio indicates greater liquidity and stability, while a lower ratio suggests higher volatility or price manipulation. 2. Identifying Retracement Patterns: • Leverages common retracement behaviors (e.g., 70% correction levels) observed in meme coins to detect potential reversal zones. • Combines this with trend line analysis for additional confirmation. 3. Leader Tokens as Indicators: • Each category is represented by its leader tokens, which have historically higher liquidity and market cap. This allows the script to accurately reflect the overall trend in each category. When to Use: • Trend Analysis: To identify which category (e.g., AI Tokens or Animal Tokens) is leading the market. • Reversal Zones: To spot potential support or resistance levels using Fibonacci zones. • Money Flow: To understand how capital is moving across different token categories in real time. Who Is This For? This script is tailored for: • Traders specializing in meme coins and DEX tokens. • Those looking for an edge in trend-based trading by analyzing market cap, volume, and retracement levels. • Anyone aiming to track money flow dynamics between different token categories. Future Updates: This is the initial version of the script. Future updates may include: • Support for additional token categories and DEX data. • More advanced pattern recognition and alerts for volume and price anomalies. • Enhanced visualization for historical data trends. With this tool, traders can combine money flow analysis with the 60-70% retracement strategy, turning it into a powerful assistant for navigating the fast-paced world of meme coins and DEX tokens. This script is designed to provide meaningful insights and practical utility for traders, adhering to TradingView’s standards for originality, clarity, and user value.

AI x Meme Impulse Tracker [QuantraSystems]AI x Meme Impulse Tracker Quantra Systems guarantees that the information created and published within this document and on the Tradingview platform is fully compliant with applicable regulations, does not constitute investment advice, and is not exclusively intended for qualified investors. Important Note! The system equity curve presented here has been generated as part of the process of testing and verifying the methodology behind this script. Crucially, it was developed after the system was conceptualized, designed, and created, which helps to mitigate the risk of overfitting to historical data. In other words, the system was built for robustness, not for simply optimizing past performance. This ensures that the system is less likely to degrade in performance over time, compared to hyper-optimized systems that are tailored to past data. No tweaks or optimizations were made to this system post-backtest. Even More Important Note!! The nature of markets is that they change quickly and unpredictably. Past performance does not guarantee future results - this is a fundamental rule in trading and investing. While this system is designed with broad, flexible conditions to adapt quickly to a range of market environments, it is essential to understand that no assumptions should be made about future returns based on historical data. Markets are inherently uncertain, and this system - like all trading systems - cannot predict future outcomes. Introduction The AI x Meme Impulse Tracker is a cutting-edge, fast-acting rotational algorithm designed to capitalize on the strength of assets within pre-selected categories. Using a custom function built on top of the RSI Pulsar, the system measures momentum through impulses rather than traditional trend following methods. This allows for swifter reallocations based on short bursts of strength. This system focuses on precision and agility - making it highly adaptable in volatile markets. The strategy is built around three independent asset categories - with allocations only made to the strongest asset in each - ensuring that capital movement (in particular between blockchains) is kept to a minimum for efficiency purposes while maintaining exposure to the highest performing tokens. Legend Token Inputs: The Impulse Tracker is designed with dynamic asset selection - allowing traders to customize the inputs for each category. This feature enables flexible system management, as the number of active tokens within each category can be adjusted at any time. Whether the user chooses the default of 13 tokens per category, or fewer, the system will automatically recalibrate. This ensures that all calculations, from relative strength to individual performance assessments, adjust as required. Disabled tokens are treated by the system as if they don’t exist - seamlessly updating performance metrics and the Impulse Tracker’s allocation behavior to maintain the highest level of efficiency and accuracy. System Equity Curve: The Impulse Tracker plots both the rotational system’s equity and the Buy-and-Hold (or ‘HODL’) benchmark of Bitcoin for comparison. While the HODL approach allocates the entire portfolio to Bitcoin and functions as an index to compare to, the Impulse Tracker dynamically allocates based on strength impulses within the chosen tokens and categories. The system equity curve is representative of adding an equal capital split between the strongest assets of each category. The relative strength system does handle ‘ties’ of strength - in this situation multiple tokens from a single category can be included in the final equity curve, with the allocated weight to that category split between the tied assets. TABLES: Equity Stats: This table is held in Quantra System's typical UI design language. It offers a comprehensive snapshot of the system’s performance, with key metrics organized to help traders quickly assess both short-term and cumulative results. The left side provides details on individual asset performance, while the right side presents a comparison of the system’s risk-adjusted metrics against a simple BTC Hodl strategy. The leftmost column of the Equity Stats table showcases performance indicators for the system’s current allocations. This provides quick identification of the current strongest tokens, based on confirmed and non-repainting data as soon as the current opens and the last bar closes. The right-hand side compares the performance differences between the system and Hodl profits, both on a cumulative basis and analyzing only the previous bar. The total number of position changes is also tracked in this table - an important metric when calculating total slippage and should be used to determine how ‘hands-on’ the strategy will be on the current timeframe. The lower part of the table highlights a direct comparison of the AI x Memes Impulse strategy with buy-and-hold Bitcoin. The risk adjusted performance ratios, Sharpe, Sortino and Omega, are shown side by side, as well as the maximum drawdown experienced by both strategies within the set testing window. Screener Table: This table provides a detailed breakdown of the performance for each asset that has been the strongest in its category at some point and thus received an allocation. The table tracks several key metrics for each asset - including returns, volatility, Sharpe ratio, Sortino ratio, Omega ratio, and maximum drawdown. It also displays the signals for both current and previous periods, as well as the assets weight in the theoretical portfolio. Assets that have never received a signal are also included, giving traders an overview of which assets have contributed to the portfolio's performance and which have not played a role so far. The position changes cell also offers important insights, as it shows the frequency of not just total position changes, but also rebalancing events. Detailed Slippage Table: The Detailed Slippage Table provides a comprehensive breakdown of the calculated slippage and fees incurred throughout the strategy’s operations. It contains several key metrics that give traders a granular view of the costs associated with executing the system: Selected Slippage - Displays the current slippage rate, as defined in the input menu. Removal Slippage - This accounts for any slippage or fees incurred when removing an allocation from a token. Reallocation Slippage - Tracks the slippage or fees when reallocating capital to existing positions. Addition Slippage - Measures the slippage or fees incurred when allocating capital to new tokens. Final Slippage - Is the sum of all the individual slippage points and provides a quick view of the total slippage accounted for by the system. The table is also divided into two columns: Last Transaction Slippage + Fees - Displays any slippage or fees incurred based on position changes within the current bar. Total Slippage + Fees - Shows the cumulative slippage and fees incurred since the portfolio’s selected start date. Visual Customization: Several customizable features are included within the input menu to enhance user experience. These include custom color palettes, both preloaded and user-selectable. This allows traders to personalize the visual appearance of the tables, ensuring clarity and consistency with their preferred interface themes and background coloring. Additionally, users can adjust both the position and sizes of all the tables - enabling complete tailoring to the trader’s layout and specific viewing preferences and screen configurations. This level of customization ensures a more intuitive and flexible interaction with the system’s data. Core Features and Methodologies Advanced Risk Management - A Unique Filtering Approach: The Equity Curve Activation Filter introduces an innovative way to dynamically manage capital allocation, aligning with periods of market trend strength. This filter is rooted in the understanding that markets move cyclically - altering between periods trending and mean-reverting periods. This cycle is especially pronounced in the crypto markets, where strong uptrends are often followed by prolonged periods of sideways movements or corrections as participants take profits and momentum fades. The Cyclical Nature of Markets and Trend Following: Financial markets do not trend indefinitely. Each uptrend or downtrend, whether over high and low timeframes, tends to culminate in a phase where momentum exhausts - leading to the sideways or corrective phases. This cycle results from the natural dynamics of market participants: during extended trends, more participants jump in, riding the momentum until profit taking causes the trend to slow down or reverse. This cyclical behavior occurs across all timeframes and in all markets - making it essential to adapt trading strategies in attempt to minimize losses during less favorable conditions. In a trend following system, profitability often mirrors this cyclical pattern. Trend following strategies thrive when markets are moving directionally, capturing gains as price moves with strength in a single direction. However in phases where the market chops sideways, trend following strategies will usually experience drawdowns and reduced returns due to the impersistent nature of any trends. This fluctuation in trend following profitability can actually serve as one of the best coincident indicators of broader market regime change - when profitability begins to fade, it often signals a transition to drawn out unfavorable trend trading conditions. The Equity Curve as a Market Signal Within the Impulse Tracker, a continuous equity curve is calculated based upon the system's allocation to the strongest tokens. This equity curve effectively tracks the system’s performance under all market conditions. However, instead of solely relying on the direct performance of the selected tokens, the system applies additional filters to analyze the trend strength of this equity curve itself. In the same way you only want to purchase an asset that is moving up in price, you only want to allocate capital to a strategy whose equity curve is trending upwards! The Equity Curve Activation Filter consistently monitors the trend of this equity curve through various filter indicators, such as the “Wave Pendulum Trend”, the “Quasar QSM” and the “MAQSM” (an aggregate of multiple types of averages). These filters help determine whether the equity curve is trending upwards, signaling a favorable period for trend following. When the equity curve is in a positive trend, capital is allocated to the system as normal - allowing it to capture gains during favorable market conditions, Conversely, when the trend weakens and the equity curves begins to stagnate or decline, the activation filter shifts the system into a “cash” positions - temporarily halting allocations in order to prevent market exposure during choppy or mean reverting phases. Timing Allocation With Market Conditions This unique filtering approach ensures that the system is primarily active during periods when market trends are most supportive. By aligning capital allocations with the uptrend in trend following profitability, the system is designed to enter during periods of strong momentum and move to cash when momentum with the equity curve wanes. This approach reduces the risk of overtrading in less favorable conditions and preserves capital for the next favorable trend. In essence the Equity Curve Allocation Filter serves as a dynamic risk management layer that leverages the cyclicality of trend following profitability in order to navigate shifting market phases. Sensitivity and Signal Responsiveness: The Quasar Sensitivity Setting allows users to fine-tune the system’s responsiveness to asset signals. High sensitivity settings lead to quicker position changes, making the system highly reactive to short term strength impulses. This is especially useful in fast moving markets where token strength can shift rapidly. The Sensitive setting might be more applicable to higher volatility or lower market cap assets - as the increased volatility increases the necessity of faster position cutting in order to front run the crowd. Of course - a balanced approach is ideal, as if the signals are too fast there will be too many whips and false signals. (And extra fees + slippage!) The benefit of this script is because of the advanced slippage calculations, false signals are sufficiently punished (unlike systems without fees or slippage) - so it will become immediately apparent if the false signals have a significantly detrimental impact on the system’s equity curve. Asset specific signals within each category are re-evaluated after the close of each bar to ensure that capital is always allocated to the highest performing asset. If a token’s momentum begins to fade the system swiftly reallocates to the next strongest asset within that category. Category Filter - Allocates only to the Strongest Asset per group One of the core innovations of the AI x Meme Impulse Tracker is the customizable Category Filter, which ensures that only the strongest-performing asset within each predefined group receives capital allocation. This approach not only increases the precision of asset selection but also allows traders to tailor the system to specific token narratives or categories. Sectors can include trending themes such as high-attention meme tokens, AI-driven tokens, or even categorize assets by blockchain ecosystems like Ethereum, Solana, or Base chain. This flexibility enables users to align their strategies with the latest market narratives or to optimize for specific groups, focusing on high-beta tokens within well defined sectors for a more targeted exposure. By keeping the focus on category leaders, the system avoids diluting its impact across underperforming assets, thereby maximizing capital efficiency and reducing unnecessary trading costs. Dynamic Asset Reallocation: Dynamic reallocation ensures that the system remains nimble and adapts to changing market conditions. Unlike slower systems, the Quasar method continually monitors for changes in asset strength and reallocates capital accordingly - ensuring that the system is always positioned in the highest performing assets within each category. Position Changes and Slippage: The Impulse Tracker places a strong emphasis on realistic simulation, prioritizing accuracy over inflated backtest results. This approach ensures that slippage is accounted for in a more aggressive manner than what may be experienced in real-world execution. Each position change within the system - whether it’s buying, selling, reallocating, or rebalancing between assets - incurs slippage. Slippage is applied to both ends of every transaction: when a position is entered and exited, and when reallocating capital from one token to another. This dynamic behavior is further enhanced by a customizable slippage/fees input, allowing users to simulate realistic transaction costs based on their own market conditions and execution behaviors. The slippage model works by applying a weighted slippage to the equity curve, taking into account the actual amount of capital being moved. Slippage is not applied in a blanket manner but rather in proportion to the allocation changes. For example, if the system reallocates from a single 100% position to two 50% allocations, slippage will be applied to the 50% removed from the first asset and the 50% added to the new asset, resulting in a 1x slippage multiplier. This process becomes more granular when multiple assets are involved. For instance, if reallocating from two 50% positions to three 33% positions, slippage will be incurred on each of the changes, but at a reduced rate (⅔ x slippage), reflecting the smaller percentage of portfolio equity being moved. The slippage model accounts for all types of allocation shifts, whether increasing or decreasing the number of tokens held, providing a realistic assessment of system costs. Here are some detailed examples to illustrate how slippage is calculated based on different scenarios: 100% → 50% / 50%: 1x slippage applied to both position changes (2 allocation changes). 50% / 50% → 33% / 33% / 33%: ⅔ x slippage multiplier applied across 3 allocation changes. 33% / 33% / 33% → 100%: 4/3 x slippage multiplier applied across 3 allocation changes. In practice, not every position change will be rebalanced perfectly, leading to a lower number of transactions and lower costs in practice. Additionally, with the use of limit orders, a trader can easily reduce the costs of entering a position, as well as ensuring a competitive entry price. By simulating slippage in this granular manner, the system captures the absolute maximum level of fees and slippage, in order to ensure that backtest results lean towards an underrepresentation - opposed to inflated results compared with practical execution. A Special Note on Slippage In the image above, the system has been applied to four different timeframes - 20h, 15h, 10h, and 5h - using identical settings and a selected slippage amount of 2%. By isolating a recent trend leg, we can illustrate an important concept: while the 15h timeframe is more profitable than the 20h timeframe, this difference stems from a core trading principle. Lower timeframes typically provide more data points and allow for quicker entries and exits in a robust system. This often results in reduced downside and compounding of gains. However, slippage, fees, and execution constraints are limiting factors, especially in volatile, low-cap cryptocurrencies. Although lower timeframes can improve performance by increasing trade frequency, each trade incurs heavy slippage costs that accumulate - impacting the portfolio’s capital at a compounding rate. In this example, the chosen slippage rate of 2% per trade is designed to reflect the realistic trading costs, emphasizing how lower timeframe trading comes at the cost of increased slippage and fees Finding the optimal balance between timeframe and slippage impact requires careful consideration of factors such as portfolio size, liquidity of selected tokens, execution speed, and the fee rate of the exchange you execute trades on. Equity Curve and Performance Calculations To provide a benchmark, the script also generates a Buy-and-Hold (or "HODL") equity curve that represents a complete allocation to Bitcoin. This allows users to easily compare the performance of the dynamic rotation system with that more traditional benchmark strategy. The script tracks key performance metrics for both the dynamic portfolio and the HODL strategy, including: Sharpe Ratio The Sharpe Ratio is a key metric that evaluates a portfolio’s risk-adjusted return by comparing its ‘excess’ return to its volatility. Traditionally, the Sharpe Ratio measures returns relative to a risk-free rate. However, in our system’s calculation, we omit the risk-free rate and instead measure returns above a benchmark of 0%. This adjustment provides a more universal comparison, especially in the context of highly volatile assets like cryptocurrencies, where a traditional risk-free benchmark, such as the usual 3-month T-bills, is often irrelevant or too distant from the realities of the crypto market. By using 0% as the baseline, we focus purely on the strategy's ability to generate raw returns in the face of market risk, which makes it easier to compare performance across different strategies or asset classes. In an environment like cryptocurrency, where volatility can be extreme, the importance of relative return against a highly volatile backdrop outweighs comparisons to a risk-free rate that bears little resemblance to the risk profile of digital assets. Sortino Ratio The Sortino Ratio improves upon the Sharpe Ratio by specifically targeting downside risk and leaves the upside potential untouched. In contrast to the Sharpe Ratio (which penalizes both upside and downside volatility), the Sortino Ratio focuses only on negative return deviations. This makes it a more suitable metric for evaluating strategies like the AI x Meme Impulse Tracker - that aim to minimize drawdowns without restricting upside capture. By measuring returns relative to a 0% baseline, the Sortino ratio provides a clearer assessment of how well the system generates gains while avoiding substantial losses in highly volatile markets like crypto. Omega Ratio The Omega Ratio is calculated as the ratio of gains to losses across all return thresholds, providing a more complete view of how the system balances upside and downside risk even compared to the Sortino Ratio. While it achieves a similar outcome to the Sortino Ratio by emphasizing the system's ability to capture gains while limiting losses, it is technically a mathematically superior method. However, we include both the Omega and Sortino ratios in our metric table, as the Sortino Ratio remains more widely recognized and commonly understood by traders and investors of all levels. Usage Summary: While the backtests in this description are generated as if a trader held a portfolio of just the strongest tokens, this was mainly designed as a method of logical verification and not a recommended investment strategy. In practice, this system can be used in multiple ways. It can be used as above, or as a factor in forming part of a broader asset selection system, or even a method of filtering tokens by strength in order to inform a day trader which tokens might be optimal to look for long-only trading setups on an intrabar timeframe. Final Summary: The AI x Meme Impulse Tracker is a powerful algorithm that leverages a unique strength and impulse based approach to asset allocation within high beta token categories. Built with a robust risk management framework, the system’s Equity Curve Activation Filter dynamically manages capital exposure based on the cyclical nature of market trends, minimizing exposure during weaker phases. With highly customizable settings, the Impulse Tracker enables precise capital allocation to only the strongest assets, informed by real-time metrics and rigorous slippage modeling in order to provide the best view of historical profitability. This adaptable design, coupled with advanced performance analytics, makes it a versatile tool for traders seeking an edge in fast moving and volatile crypto markets.

Memecoin Tracker Memecoin Z-Score Tracker with Buy/Sell Table - Technical Explanation How it Works: This indicator calculates the Z-scores of various memecoins based on their price movements, using historical funding rates across multiple exchanges. A Z-score measures the deviation of the current price from its moving average, expressed in standard deviations. This provides insight into whether a coin is overbought (positive Z-score) or oversold (negative Z-score) relative to its recent history. Key Components: - Z-Score Calculation - The lookback period is dynamically adjusted based on the chart’s timeframe to ensure consistency across different time intervals: - For lower timeframes (e.g., minutes), the base lookback period is scaled to match approximately 240 minutes. - For daily and higher timeframes, the base lookback period is fixed (e.g., 14 bars). Memecoin Selection: The indicator tracks several popular memecoins, including DOGE, SHIB, PEPE, FLOKI, and others. Funding rates are fetched from exchanges like Binance, Bybit, and MEXC using the request.security() function, ensuring accurate real-time price data. Thresholds for Buy/Sell Signals: Users can set custom Z-score thresholds for buy (oversold) and sell (overbought) signals: Default upper threshold: 2.5 (indicates overbought condition). Default lower threshold: -2.5 (indicates oversold condition). When a memecoin’s Z-score crosses above or below these thresholds, it signals potential buy or sell conditions. Buy/Sell Table: A table with two columns (BUY and SELL) is dynamically populated with memecoins that are currently oversold (buy signal) or overbought (sell signal). Each column can hold up to 20 entries, providing a clear overview of current market opportunities. Visual Feedback: The Z-scores of each memecoin are plotted as a line on the chart, with color-coded feedback: Red for overbought (Z-score > upper threshold), Green for oversold (Z-score < lower threshold), Other colors indicate neutral conditions. Horizontal lines representing the upper and lower thresholds are plotted for reference. How to Use It: Adjust Thresholds: You can modify the upper and lower Z-score thresholds in the settings to customize sensitivity. Lower thresholds will increase the likelihood of triggering buy/sell signals for smaller price deviations, while higher thresholds will focus on more extreme conditions. View Real-Time Signals: The table shows which memecoins are currently oversold (buy column) or overbought (sell column), updating dynamically as price data changes. Traders can monitor this table to identify trading opportunities quickly. Use with Different Timeframes: The Z-score lookback period adjusts automatically based on the chart's timeframe, making this indicator suitable for intraday and long-term traders. Use shorter timeframes (e.g., 1-minute, 5-minute charts) for faster signals, while longer timeframes (e.g., daily, weekly) may yield more stable, trend-based signals. Who It Is For: Short-Term Traders: Those looking to capitalize on short-term price imbalances (e.g., day traders, scalpers) can use this indicator to identify quick buy/sell opportunities as memecoins oscillate around their moving averages. Swing Traders: Swing traders can use the Z-score tracker to identify overbought or oversold conditions across multiple memecoins and ride the reversals back toward equilibrium. Crypto Enthusiasts and Memecoin Investors: Anyone involved in the volatile memecoin market can use this tool to better time entries and exits based on market extremes. This indicator is for traders seeking quantitative analysis of price extremes in memecoins. By tracking the Z-scores across multiple coins and dynamically updating buy/sell opportunities in a table, it provides a systematic approach to identifying trade setups.