Universal Signal Backtester [LuxAlgo]The Universal Signal Backtester indicator is a tool designed to simulate and analyze the performance of virtually any trading signal, ranging from moving average crossovers to external indicator triggers. By providing a professional-grade backtesting environment directly on the chart, it allows users to optimize entry logic, exit strategies, and cost management in real-time.
🔶 USAGE
The script operates by selecting a Source Mode to define how trades are initiated. Users can choose between built-in MA crosses, external source crossovers, or discrete signal triggers from other indicators. Once a signal is detected, the script simulates a trade using up to three Take Profit (TP) and three Stop Loss (SL) levels.
🔹 Trade Visualization
* Sign Posts: Upon entry, the script plots a "Sign Post" label below or above the bar. This label dynamically suggests which TP level is currently the most effective based on the selected metric (e.g., Hit Rate or Expected Profit).
* Active Exit Lines: Horizontal dashed lines extend from the entry point to visualize the selected TP and SL targets. These lines update in real-time and mark hits with a checkmark (✓) or an "X" (✗).
* MA Ribbon: When using predefined crosses, a gradient ribbon is displayed to visualize the trend strength and crossover points.
🔹 Signal Modes
* Predefined Crosses: Quickly test standard strategies like the 9/21 EMA, 12/26 EMA, or the Golden/Death Cross (50/200 SMA).
* External Sources (Crossovers): Select two external plots (e.g., RSI and a level, or two custom MAs) to test their crossover performance.
* External Signals (Triggers): Connect the script to discrete signals such as Plotshapes or Boolean conditions. This is ideal for testing "Buy" and "Sell" signals from specialized indicators.
🔶 DETAILS
🔹 Advanced Analytics Dashboard
The indicator features a robust dashboard providing institutional-level metrics:
* Core Metrics: Total trades, Win Rate, Profit Factor, Sharpe Ratio, and Recovery Factor.
* Equity Curve: A sparkline representation of the account growth over time.
* Hourly Histogram: A performance breakdown by the hour of the day, helping identify the most profitable trading sessions.
* Heatmaps: A "Days of the Week" or "Monthly" heatmap that colors cells based on profitability, allowing for the identification of seasonal or periodic performance patterns.
🔹 Cost Simulation
To ensure realistic results, the script includes a cost engine. Users can select from profiles like Forex, Crypto, or Stocks to automatically apply industry-standard spreads and commissions, or input manual values to match a specific broker's fee structure.
🔶 SETTINGS
🔹 Source Settings
Source Mode: Determines the logic for trade entries (Predefined, External Crossover, or External Trigger). Signal Logic: Defines how external triggers are interpreted (e.g., Value Changes, Crosses 0, or Not NA). Trade Direction: Filters signals to allow only Longs, only Shorts, or Both.
🔹 Filters
Use ATR Choppiness Filter: When enabled, the script ignores signals that occur during low-volatility "choppy" periods.
🔹 Target Settings
Distance Type: Sets the measurement unit for TP/SL levels (ATR, Ticks, or Points). Take Profit (1-3): Toggles and sets the distance for up to three partial take-profit levels. Stop Loss (1-3): Toggles and sets the distance for up to three stop-loss levels.
🔹 Costs
Simulate Spread & Commission: Enables the cost engine for more realistic PnL calculations. Cost Profile: Presets for Forex, Crypto, and Stocks, or "Manual" for custom inputs.
🔹 Dashboard & Visuals
Heatmap Period: Switches the dashboard heatmap between "Days of Week" and "Months". Suggested TP Metric: Chooses the criteria the "Sign Post" uses to suggest the best TP level. Gradient Candle Coloring: Colors candles based on the distance between the fast and slow sources. Indicator
Parallel Transport & Holonomy [DAFE]Parallel Transport & Holonomy - (⟡ PTH)
This publication presents the quantitative mechanics and theoretical architecture behind the Parallel Transport & Holonomy engine. This system models price and volume dynamics as trajectories on a continuous two-dimensional manifold. By computing the parallel transport of multi-scale momentum vectors, tracking their covariant derivatives, and integrating their path-dependent rotational deficit ( Holonomy ), the indicator identifies macro regime shifts, structural pivots, and trend continuations.
The architecture is entirely self-contained, featuring a Legit Footprint Toggle that automatically falls back to an advanced Synthetic Footprint Engine when tick-level footprint data is unavailable. It is strictly anchored by a Rolling Confidence Matrix (RCM) and modulated by an Ensemble Dynamic Volatility Scaling (DVS) engine.
The core principle of this system is geometric invariance: signal evaluation is not based on static, lagging thresholds but on the coordinate-invariant deviation of momentum from its natural geodesic path.
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Riemannian Price-Volume Manifold & Metric Tensor
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We define the market state as a coordinate point on a two-dimensional Riemannian manifold
M, where the coordinates are:
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Parallel Transport & Covariant Derivatives
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Let V be a momentum vector on the tangent space of the manifold, constructed at three distinct temporal scales: Fast (vecFast), Medium (vecMed), and Slow (vecSlow). The coordinate components of the vector scale k are computed as:
Geodesic Deviation
A path is geodesic if its covariant derivative along the direction of travel is zero (representing the path of least resistance on the manifold). We define the geodesic deviation Zgeodesic as the rolling z-score of the absolute transport deviation:
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Holonomy & Phase Accumulation
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Holonomy describes the geometric phenomenon where parallel transporting a vector along a closed loop returns a vector with a changed orientation. This angular deficit directly measures the total curvature enclosed by the loop.
Holonomy Group Classifications
Based on the magnitude of the accumulated rotation (HolonomyNorm), the local coordinate space is classified into four geometric groups:
Trivial (Norm<0.10): The manifold is flat; parallel transport is path-independent, indicating quiet consolidated ranges.
Abelian (Norm<0.25): Minor commutative drift; the vector field undergoes predictable linear shifts.
Non-Abelian (Norm<0.50): Significant non-commutative curvature. Parallel transport is highly path-dependent, signaling complex trend development and institutional accumulation.
Singular (Norm≥0.50): Severe rotational deficit. The manifold geometry has collapsed or rotated sharply, marking high-probability reversal peaks and structural transitions.
Holonomy Regime Shifts
A Regime Shift is triggered when HolonomyNorm exceeds the user-defined threshold (i_holoThresh). Depending on the sign of the accumulated loop (sgn(H)) and the divergence between the fast and slow vectors, the engine classifies the transition into one of the following states:
Bull Rotation : Enclosed path yields a positive rotational deficit combined with positive multi-scale momentum.
Bear Rotation : Enclosed path yields a negative rotational deficit combined with negative multi-scale momentum.
Geodesic Break : Extreme localized acceleration causing the momentum field to tear away from its expected trajectory.
Scale Divergence : High-frequency (fast) and low-frequency (slow) vectors rotate in opposite directions, enclosing a singular point.
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Rolling Confidence Matrix (RCM)
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The Rolling Confidence Matrix (RCM) acts as a structural anchor. It takes multi-scale geometric indicators from the transport engine and compiles them into a state-space model that dynamically controls signal permissions.
State Space Representation
The matrix maintains a rolling assessment of market structure across three distinct states:
Established (State 0): The trend is structurally intact; manifold bands are compressed, and parallel transport confirms high directional persistence.
Transition (State 1): Price is approaching key structural levels or experiencing a holonomy spike. Manifold bands widen, and standard signals are gated.
Damaged (State 2): Price has broken through primary defensive corridors. Structural confidence is compromised; bands expand by the Damage Band Expansion multiplier
(i_damageWiden), and counter-trend positions are heavily penalized.
External Evidence Injection
On every bar, the system injects external geometric parameters from the manifold:
This evidence is integrated into the RCM, outputting structural permissions (allowLong, allowShort) and score modulations that are applied to the final signal scoring calculations.
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Ensemble Dynamic Volatility Scaling (DVS)
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To adapt the mathematical models to varying market volatility, the script runs three independent volatility estimators concurrently on every bar:
Ensemble Combinatorics
The baseline values are calculated as moving averages over the memory period. The script then computes the ratios of the current estimators to their historical baselines:
This ratio dynamically scales the width of the holographic manifold bands and the RCM damage thresholds: during highly volatile regimes, thresholds are expanded to prevent whipsaws; during compression regimes, thresholds are tightened to capture early breakouts.
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Footprint & Synthetic Fallback Engine
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The script includes a Use Legit Footprint toggle (i_useFp). When enabled on charts supporting tick databases, the system requests official exchange-traded order flow data:
fp=request.footprint(i_fpTicks,i_fpVA)
The Advanced Synthetic Footprint Engine
If the user deactivates the footprint toggle, or if the asset/timeframe does not support tick database access (e.g., historical bars or Forex), the Synthetic Footprint Fallback Engine** takes over, simulating volume-at-price and directional delta using high-frequency mathematical approximations:
This estimated delta reconstructs the synthetic buy and sell pressures:
Kyle's Lambda (Price Impact & Liquidity Risk)
Using these directional volumes, we calculate Kyle's price impact coefficient λ, measuring the price change per unit of volume flow:
High values of λ represent illiquid, thin, or toxic conditions. The system calculates the z-score of lambda (Zλ). If Zλ > 2.0, signal scores are dynamically penalized to protect capital from low-liquidity slippage.
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Structural Scoring & Signal Gating
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The system generates a directional Signal Score using a two-tier quantitative scoring process.
Stage 1: Structural Base (Minimum of one required)
Siege Corridor Proximity : Price is within 1.2 ⋅ ATR of a verified support or resistance level.
ScoreBase = 0.28+(Hold Probability⋅0.18)
EMA21 Geodesic Pullback : Price pulls back to the EMA21 in trend alignment.
ScoreBase = 0.25
RSI Extreme Exhaustion : RSI is below 25 (for longs) or above 75 (for shorts).
ScoreBase = 0.28
Stage 2: Confirmations (Additive)
Multi-pattern confluence (2+ patterns matching): +0.15
Pressure bar strength exceeds thresholds: +0.08 to +0.15
Metric curvature aligns with structural turn: +0.10
Volume expansion exceeds historical average: +0.10
Holonomy phase shifts or loop spikes: +0.12
Directional Scaling & Gating
The score is adjusted based on trend alignment. With-Trend (WT) setups (longs above the EMA50, shorts below) are evaluated using standard thresholds. Counter-Trend (CT) setups face a strict scaling penalty:
ScoreCT = ScoreRaw ⋅ clamp(1.0−Trend Strength⋅0.15,0.45,1.0)
Additionally, the score passes through three gates:
Liquidity Penalty : Reduced by up to 30% if order flow thickness is classified as Desert or Frozen.
Kyle Lambda Gate : Penalized if price impact is highly volatile (Zλ > 2.0).
Danger Gate : Score is multiplied by 0.60 if the composite risk danger index exceeds 75.
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Input Reference
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═══ GEOMETRY ENGINE ═══
Hurst Period (30 to 200, default 80): Lookback window for R/S calculations to evaluate trend persistence.
Pivot Length (2 to 15, default 5): Lookback/lookahead buffer for swing pivot detection.
TF Preset (Auto, 1m-5m Scalp, 10m-15m Intra, 30m-1H Swing, 4H-D Position): Set the timeframe scaling preset. Auto dynamically scales lookbacks based on the current chart timeframe.
Use Legit Footprint (Boolean, default true): Toggles whether to query exchange tick database for real footprint data.
Footprint Ticks/Row (1 to 500, default 100): Height of the footprint rows in tick units.
Footprint VA % (50 to 90, default 70): Percentage of volume enclosed within the Value Area.
═══ TRANSPORT & HOLONOMY ═══
Vector Fast Scale (3 to 21, default 8): Periods for tracking short-term momentum vectors.
Vector Medium Scale (10 to 55, default 21): Periods for intermediate momentum geometry.
Vector Slow Scale (21 to 144, default 55): Baseline vector window representing the macro manifold.
Holonomy Window (13 to 89, default 34): Size of the temporal loop used to integrate local deviations.
Holonomy Spike Threshold (0.10 to 0.90, default 0.35): Sensitivity threshold for identifying rotational shifts.
Geodesic Deviation Sensitivity (0.5 to 3.0, default 1.5): Sensitivity for flagging off-path momentum.
Enable Continuation Signals (Boolean, default true): Toggles secondary pullback entries in strong trends.
═══ ROLLING CONFIDENCE MATRIX ═══
Block Transition Signals (Boolean, default true): If active, blocks standard entry signals when the matrix is in transition state.
Conf Band Tightening Factor (0.0 to 1.0, default 0.35): Compression multiplier for manifold bands under high confidence.
Damage Band Expansion (1.0 to 1.5, default 1.15): Expansion multiplier for manifold bands under structural damage.
═══ DYNAMIC VOLATILITY SCALING (DVS) ═══
Enable Ensemble DVS Engine (Boolean, default true): Toggles whether to scale target multiples and thresholds by volatility.
DVS Method (ATR Ratio, Parkinson, Garman-Klass, Ensemble): Method for calculating real-time volatility.
DVS Memory Period (20 to 200, default 100): Baseline lookback window for historical volatility.
DVS Sensitivity (0.3 to 1.5, default 0.7): Exponent scaling parameter for the DVS ratio.
═══ SIGNAL GATING ═══
Signal Threshold (0.10 to 0.85, default 0.40): Minimum score required to fire a signal.
Cooldown Bars (1 to 50, default 6): Minimum bar spacing between consecutive signals.
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Visual System & Dashboard Interpretation
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Transport Field Manifold : A multi-layered band surrounding price. It contracts during clean, high-confidence geodesic flow and expands during high-entropy, damaged, or volatile regimes. Its color is determined by the RCM state: Green for bullish stability, Red for bearish, and Purple during transitions.
Siege Corridors : Plotted as horizontal support (Green) and resistance (Red) zones with an ATR-scaled boundary. At the right-hand side of the chart, these corridors display critical metrics: the total historical hit count, the estimated break probability (Pbreak), and the fail/reversal probability (Pfail).
Vector Field Lines : Small arrow-headed lines plotted on every third bar. They show the mathematical direction of the Fast momentum vector. They visually represent the difference between the actual observed path of the market and the expected parallel-transported baseline.
Holonomy Phase Ring : An indicator plotted at the right edge of the chart displaying the current phase angle (e.g., ◐ 144∘).
Glitch Markers: Flagged on the chart as ◈ GLITCH when anomalous pricing data or sudden coordinate jumps are detected on the manifold.
Dashboard: Displays real-time metric readings from all quantitative modules:
Tensor Metrics: Curvature, Entropy, Hurst Exponent, and Price Pressure.
Volatility: Dynamic DVS Ratio, active estimator method, and scaling state.
RCM Brain: State classification, integrity index, and gate permissions.
Performance: Total trades, win rate, total profit (R-multiple), profit factor, and max drawdown.
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Limitations and Disclaimers
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This tool is for analytical purposes only and does not predict future market direction.
Historical Simulation: The built-in simulator represents hypothetical performance on historical data. Past results do not guarantee future performance.
Volume Approximations: The synthetic footprint engine uses OHLCV approximations to estimate volume-at-price and directional delta. It is not an actual connection to L2 exchange data feeds or a central clearing book.
Persistence: Reinforcement learning weights are state-space variables that exist within the local execution thread; they reset to 1.0 on every chart load or browser reload.
— Dskyz. Trade with insight. Trade with anticipation. (It's lonely at the top) Indicator
Trend Volatility RegimeThe Trend Volatility Regime is an all-in-one trend-following model that identifies changes in the market regime by combining moving-average crossover signals with volatility-adaptive trailing stops. It features an integrated backtesting engine that provides institutional-grade insights into historical strategy performance, along with a built-in alert system that notifies investors in real time when regime changes occur. The model integrates seamlessly into the price chart and presents backtest results in a clear, color-coded table benchmarked against buy-and-hold.
At its core, the model combines two complementary trend detection components to determine the prevailing market regime. The first component identifies the underlying structural trend using a volatility-adjusted moving-average crossover based on the spread between fast and slow moving averages. The second component identifies trend reversals using an adaptive trailing stop based on changes in price and volatility. Bullish and bearish regimes occur when both crossover and volatility signals are directionally aligned, while conflicting signals result in neutral regimes.
Bullish Crossover Signal = (Fast MA – Slow MA) > (ATR × Trend Margin)
Bearish Crossover Signal = (Fast MA – Slow MA) < –(ATR × Trend Margin)
Bullish Volatility Signal = Price > (Highest Price – (Volatility × Stop Factor))
Bearish Volatility Signal = Price < (Lowest Price + (Volatility × Stop Factor))
By default, the model applies an asymmetric regime design in which conflicting signals default to a bullish regime unless half-equity positions are enabled in the menu. This asymmetric design reflects the tendency of risk assets to deteriorate gradually while recovering more abruptly. The moving-average component captures the slower deterioration typically observed during market tops, while the trailing stop component responds more dynamically to faster reversals typically observed at market bottoms. This helps reduce overreaction to corrections during uptrends while still allowing for faster re-entry following sharp recoveries. To evaluate the performance of different parameter configurations, the model includes a built-in table with the following metrics:
CAGR = Compounded Annual Growth Rate.
Excess = CAGR in excess of buy-and-hold.
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Turnover = Average annualized change in exposure.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period, the preferred trend type, volatility type, and regime visualization. Supported regime visualizations include line, candle, and shaded background. Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported price sources include Close, HL2, HLC3, and OHLC4. The table follows an intuitive color-coded logic that allows for quick performance comparison against buy-and-hold (B&H):
CAGR = Green indicates above 0%, while red indicates below 0%.
Excess = Green indicates above 0%, while red indicates below 0%.
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Trend Volatility Regime is a comprehensive trend-following tool designed to help investors stay on the right side of the market by identifying key changes in the market regime. By combining volatility-adjusted moving-average crossover signals with adaptive volatility-based trailing stops, the model seeks to maximise participation during uptrends while reducing exposure during sustained downtrends. While the model provides valuable historical insights, users should remain mindful that past results may not necessarily persist under future market conditions. Indicator
Strategy Sensitivity MatrixThe Strategy Sensitivity Matrix is an institutional-grade backtesting tool designed to evaluate the robustness and parameter sensitivity of trend-following strategies. It enables users to compare the historical performance of a broad range of parameter combinations across multiple metrics to assess the overall stability of the selected strategy. The model displays the complete backtest landscape in a structured, color-coded matrix that allows investors to quickly identify robust parameter regions and evaluate historical performance stability across parameter combinations.
At its core, the matrix systematically evaluates a wide range of parameter combinations, where every individual cell represents the backtest result for one unique parameter configuration. Users can switch between volatility-based strategies and moving-average strategies. In volatility mode, the matrix rows represent volatility lengths and the matrix columns represent volatility factors. In crossover mode, the rows represent fast moving-average lengths and the columns represent slow moving-average lengths. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported moving-average types include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), and Weighted Moving Average (WMA). Supported display metrics include:
CAGR = Compounded Annual Growth Rate.
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Martin = CAGR relative to the Ulcer Index (UI).
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Alpha (α) = Excess annualized risk-adjusted returns.
Expectancy = Average expected return per trade.
Profit Factor = Total gross profit per unit of losses.
Win Rate = Ratio of profitable trades to total trades.
Trades/Year = Average number of trades per year.
The matrix follows an intuitive percentile-based coloring framework that dynamically compares the relative performance and stability of all parameter combinations. Stronger values above or equal to the matrix median are highlighted in green, with bright green representing the top 10% of all parameter combinations. Weaker values below the matrix median are highlighted in orange, while red represents objectively weak performance based on the selected metric. Broad clusters of consistently strong results generally suggest lower parameter sensitivity and potentially greater robustness, while isolated peaks generally suggest elevated parameter sensitivity.
The summary table displayed above the matrix provides a broader distribution-level statistical overview of results across all parameter combinations. This structure allows investors to evaluate whether strong historical performance appears statistically widespread or narrowly concentrated across the parameter landscape. Stable parameter landscapes generally exhibit lower standard deviation, similar median and average values, and smaller performance gaps between the best and top 10% parameter combinations. The summary table includes the following sections:
Start = Start month and year of the selected backtest period.
End = End month and year of the selected backtest period.
Metric = Performance metric currently displayed in the matrix.
B&H = Buy-and-hold performance for the selected metric.
Best = Best-performing parameter combination in the matrix.
Top 10% = Average value of the top 10% parameter combinations.
Median = Median value across all parameter combinations.
Average = Average value across all parameter combinations.
Std Dev = Standard deviation of all parameter combinations.
≥ B&H = Percentage of combinations equal or better than B&H.
In summary, the Strategy Sensitivity Matrix is a powerful robustness analysis tool designed to help investors make data-driven decisions when evaluating parameter combinations across trend-following strategies. By evaluating the full parameter landscape, investors can quickly determine whether strong historical performance appears broadly distributed across stable parameter regions or narrowly concentrated within isolated parameter combinations. While historical robustness can provide valuable insight into past market behavior over the selected backtest period, users should remain mindful that market structures evolve over time and that historically stable parameter regions may not necessarily persist in future market conditions. Indicator
Volatility Stop SelectorThe Volatility Stop Selector is a comprehensive trend-following tool designed to automatically identify the optimal volatility stop strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple volatility length and factor combinations, determining the best-performing configuration, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of volatility stop combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using metrics such as the Sharpe, Sortino, Martin, or Calmar ratios. The Martin ratio is particularly well suited for volatility-based risk management strategies, as it evaluates returns relative to the Ulcer Index, capturing both the depth and duration of drawdowns and therefore favoring smoother equity curves. Alternatively, users can enable manual optimization to test custom volatility length and factor settings and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the volatility length and factor displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period, the preferred volatility type, and the price source. Supported volatility types include the Average True Range (ATR), Standard Deviation (SD), and Mean Absolute Deviation (MAD). Supported price sources include Close, Heikin Ashi, HL2, HLC3, and OHLC4. To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes more robust combinations by requiring more reactive combinations to exceed this threshold. The table follows an intuitive color-coded logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Volatility Stop Selector is a powerful tool designed to help investors make data-driven decisions when selecting volatility-based trend-following strategies. By optimizing for risk-adjusted returns, investors can identify the best configurations using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of overfitting, as past results may not persist under future market conditions. Since the model continuously recalibrates to incorporate new data, the recommended length and factor may evolve over time. Indicator
Golden BOS Strategy - ChecklistA clean, mechanical on-chart checklist designed for multi-timeframe traders using the Golden BOS / Institutional Retracement Framework.
This tool helps you stay disciplined by tracking each requirement of the strategy in real time:
Included Criteria
4H Bias: Bullish or bearish macro structure
1H Structure: Push/pull phase + golden zone retracement
5M Entry Model:
Break of Structure (BOS)
5M golden zone retracement
POI validation (OB/FVG/Breaker)
Final micro BOS or rejection confirmation
Risk Filters:
Session validity (London / NY)
Red news avoidance
Stop-loss placement check
Liquidity-based target confirmation
Purpose
This overlay ensures every trade meets strict criteria before execution, removing emotion and improvisation. Ideal for backtesting, forward testing, and staying consistent during live market conditions.
Golden BOS Strategy — Description
The Golden BOS Strategy is a structured, multi-timeframe trading system designed to capture high-probability continuation moves during London and New York sessions. The strategy combines institutional concepts with Fibonacci-based retracements to identify discounted entry zones aligned with higher-timeframe direction.
Using the 4H timeframe, traders establish the daily macro bias and identify the dominant trend. The 1H chart is then used to confirm the current phase of market structure, distinguishing between impulsive “push” moves and corrective “pullback” phases. A Fibonacci retracement is applied to the most recent 1H impulse leg to define a high-value discount or premium zone where entries become valid.
Execution takes place on the 5-minute chart. Once price reaches the 1H golden zone (61.8–78.6%), a Break of Structure (BOS) is required to confirm a shift in short-term momentum. A second Fibonacci retracement is then drawn on the 5M impulse leg that caused the BOS, and price must retrace back into the 5M golden zone. Traders refine their entry using a confluence point of interest (POI) such as a Fair Value Gap (FVG), Order Block, Breaker Block, or Inverse FVG, ideally accompanied by a final micro BOS or rejection candle.
Risk management is strict and rule-driven. Stop loss is placed beyond the extreme wick of the POI, while take-profit targets are set at logical liquidity pools in the direction of the higher-timeframe trend. The strategy avoids red-folder news and only allows trades during active sessions to ensure optimal volatility and reliability.
The Golden BOS Strategy is designed to impose discipline, reduce discretionary errors, and give traders a repeatable, mechanical framework for navigating trending markets with precision. Indicator
Adaptive Trend SelectorThe Adaptive Trend Selector is a comprehensive trend-following tool designed to automatically identify the optimal moving average crossover strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple moving average combinations, determining the best performing lengths, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy.
At its core, the model systematically backtests a wide range of moving average combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using the Sharpe, Sortino, or Calmar ratios. Alternatively, users can enable manual optimization to test custom fast and slow moving average lengths and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the fast and slow lengths displayed at the top:
Sharpe = CAGR per unit of standard deviation.
Sortino = CAGR per unit of downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Return sensitivity relative to buy-and-hold.
Alpha (α) = Excess annualized risk-adjusted returns.
Win Rate = Ratio of profitable trades to total trades.
Profit Factor = Total gross profit per unit of losses.
Expectancy = Average expected return per trade.
Trades/Year = Average number of trades per year.
This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period and the preferred moving average strategy. Supported strategies include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes longer, more robust combinations by requiring shorter-length strategies to exceed this threshold. The table follows an intuitive color logic that enables quick performance comparison against buy-and-hold (B&H):
Sharpe = Green indicates better than B&H, while red indicates worse.
Sortino = Green indicates better than B&H, while red indicates worse.
Calmar = Green indicates better than B&H, while red indicates worse.
Max DD = Green indicates better than B&H, while red indicates worse.
Beta (β) = Green indicates better than B&H, while red indicates worse.
Alpha (α) = Green indicates above 0%, while red indicates below 0%.
Win Rate = Green indicates above 50%, while red indicates below 50%.
Profit Factor = Green indicates above 2, while red indicates below 1.
Expectancy = Green indicates above 0%, while red indicates below 0%.
In summary, the Adaptive Trend Selector is a powerful tool designed to help investors make data-driven decisions when selecting moving average crossover strategies. By optimizing for risk-adjusted returns, investors can confidently identify the best lengths using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of potential overfitting, as past results may not persist under future market conditions. Since the model recalibrates to incorporate new data, the recommended lengths may evolve over time. Indicator
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark.
Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets.
To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics:
CAGR = Compounded annual growth rate of returns.
Volatility = Annualized standard deviation of returns.
Sharpe = CAGR per unit of annualized standard deviation.
Sortino = CAGR per unit of annualized downside deviation.
Calmar = CAGR relative to maximum drawdown.
Max DD = Largest peak-to-trough decline in value.
Beta (β) = Sensitivity of returns relative to benchmark returns.
Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark.
Upside = Ratio of average return to benchmark return on up days.
Downside = Ratio of average return to benchmark return on down days.
Tracking = Annualized standard deviation of returns versus benchmark.
Turnover = Average sum of absolute changes in weights per year.
Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures.
In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies. Indicator
The Best Strategy Template[LuciTech]Hello Traders,
This is a powerful and flexible strategy template designed to help you create, backtest, and deploy your own custom trading strategies. This template is not a ready-to-use strategy but a framework that simplifies the development process by providing a wide range of pre-built features and functionalities.
What It Does
The LuciTech Strategy Template provides a robust foundation for building your own automated trading strategies. It includes a comprehensive set of features that are essential for any serious trading strategy, allowing you to focus on your unique trading logic without having to code everything from scratch.
Key Features
The LuciTech Strategy Template integrates several powerful features to enhance your strategy development:
•
Advanced Risk Management: This includes robust controls for defining your Risk Percentage per Trade, setting a precise Risk-to-Reward Ratio, and implementing an intelligent Breakeven Stop-Loss mechanism that automatically adjusts your stop to the entry price once a specified profit threshold is reached. These elements are crucial for capital preservation and consistent profitability.
•
Flexible Stop-Loss Options: The template offers adaptable stop-loss calculation methods, allowing you to choose between ATR-Based Stop-Loss, which dynamically adjusts to market volatility, and Candle-Based Stop-Loss, which uses structural price points from previous candles. This flexibility ensures the stop-loss strategy aligns with diverse trading styles.
•
Time-Based Filtering: Optimize your strategy's performance by restricting trading activity to specific hours of the day. This feature allows you to avoid unfavorable market conditions or focus on periods of higher liquidity and volatility relevant to your strategy.
•
Customizable Webhook Alerts: Stay informed with advanced notification capabilities. The template supports sending detailed webhook alerts in various JSON formats (Standard, Telegram, Concise Telegram) to external platforms, facilitating real-time monitoring and potential integration with automated trading systems.
•
Comprehensive Visual Customization: Enhance your analytical clarity with extensive visual options. You can customize the colors of entry, stop-loss, and take-profit lines, and effectively visualize market inefficiencies by displaying and customizing Fair Value Gap (FVG) boxes directly on your chart.
How It Does It
The LuciTech Strategy Template is meticulously crafted using Pine Script, PulseWire's powerful and expressive programming language. The underlying architecture is designed for clarity and modularity, allowing for straightforward integration of your unique trading signals. At its core, the template operates by taking user-defined entry and exit conditions and then applying a sophisticated layer of risk management, position sizing, and trade execution logic.
For instance, when a longCondition or shortCondition is met, the template dynamically calculates the appropriate position size. This calculation is based on your specified risk_percent of equity and the stop_distance (the distance between your entry price and the calculated stop-loss level). This ensures that each trade adheres to your predefined risk parameters, a critical component of disciplined trading.
The flexibility in stop-loss calculation is achieved through a switch statement that evaluates the sl_type input. Whether you choose an ATR-based stop, which adapts to market volatility, or a candle-based stop, which uses structural price points, the template seamlessly integrates these methods. The ATR calculation itself is further refined by allowing various smoothing methods (RMA, SMA, EMA, WMA), providing granular control over how volatility is measured.
Time-based filtering is implemented by comparing the current bar's time with user-defined start_hour, start_minute, end_hour, and end_minute inputs. This allows the strategy to activate or deactivate trading during specific market sessions or periods of the day, a valuable tool for optimizing performance and avoiding unfavorable conditions.
Furthermore, the template incorporates advanced webhook alert functionality. When a trade is executed, a customizable JSON message is formatted based on your webhook_format selection (Standard, Telegram, or Concise Telegram) and sent via alert function. This enables seamless integration with external services for real-time notifications or even automated trade execution through third-party platforms.
Visual feedback is paramount for understanding strategy behavior. The template utilizes plot and fill functions to clearly display entry prices, stop-loss levels, and take-profit targets directly on the chart. Customizable colors for these elements, along with dedicated options for Fair Value Gap (FVG) boxes, enhance the visual analysis during backtesting and live trading, making it easier to interpret the strategy's actions.
How It's Original
The LuciTech Strategy Template distinguishes itself in the crowded landscape of PulseWire scripts through its unique combination of integrated, advanced risk management features, highly flexible stop-loss methodologies, and sophisticated alerting capabilities, all within a user-friendly and modular framework. While many templates offer basic entry/exit signal integration, LuciTech goes several steps further by providing a robust, ready-to-use infrastructure for managing the entire trade lifecycle once a signal is generated.
Unlike templates that might require users to piece together various risk management components or code complex stop-loss logic from scratch, LuciTech offers these critical functionalities out-of-the-box. The inclusion of dynamic position sizing based on a user-defined risk percentage, a configurable risk-to-reward ratio, and an intelligent breakeven mechanism significantly elevates its utility. This comprehensive approach to capital preservation and profit targeting is a cornerstone of professional trading and is often overlooked or simplified in generic templates.
Furthermore, the template's provision for multiple stop-loss calculation types—ATR-based for volatility adaptation, and candle-based for structural support/resistance—demonstrates a deep understanding of diverse trading strategies. The underlying code for these calculations is already implemented, saving developers considerable time and effort. The subtle yet powerful inclusion of FVG (Fair Value Gap) related inputs also hints at advanced price action concepts, offering a sophisticated layer of analysis and execution that is not commonly found in general-purpose templates.
The advanced webhook alerting system, with its support for various JSON formats tailored for platforms like Telegram, showcases an originality in catering to the needs of modern, automated trading setups. This moves beyond simple PulseWire pop-up alerts, enabling seamless integration with external systems for real-time trade monitoring and execution. This level of external connectivity and customizable data output is a significant differentiator.
In essence, the LuciTech Strategy Template is original not just in its individual features, but in how these features are cohesively integrated to form a powerful, opinionated, yet highly adaptable system. It empowers traders to focus their creative energy on developing their core entry/exit signals, confident that the underlying framework will handle the complexities of risk management, trade execution, and external communication with precision and flexibility. It's a comprehensive solution designed to accelerate the development of robust and professional trading strategies.
How to Modify the Logic to Apply Your Strategy
The LuciTech Strategy Template is designed with modularity in mind, making it exceptionally straightforward to integrate your unique trading strategy logic. The template provides a clear separation between the core strategy management (risk, position sizing, exits) and the entry signal generation. This allows you to easily plug in your own buy and sell conditions without altering the robust underlying framework.
Here’s a step-by-step guide on how to adapt the template to your specific trading strategy:
1.
Locate the Strategy Logic Section:
Open the Pine Script editor in PulseWire and navigate to the section clearly marked with the comment //Strategy Logic Example:. This is where the template’s placeholder entry conditions (a simple moving average crossover) are defined.
2.
Define Your Custom Entry Conditions:
Within this section, you will find variables such as longCondition and shortCondition. These are boolean variables that determine when a long or short trade should be initiated. Replace the existing example logic with your own custom buy and sell conditions. Your conditions can be based on any combination of indicators, price action patterns, candlestick formations, or other market analysis techniques. For example, if your strategy involves a combination of RSI and MACD, you would define longCondition as (rsi > 50 and macd_line > signal_line) and shortCondition as (rsi < 50 and macd_line < signal_line).
3.
Leverage the Template’s Built-in Features:
Once your longCondition and shortCondition are defined, the rest of the template automatically takes over. The integrated risk management module will calculate the appropriate position size based on your Risk % input and the chosen Stop Loss Type. The Risk:Reward ratio will determine your take-profit levels, and the Breakeven at R feature will manage your stop-loss dynamically. The time filter (Use Time Filter) will ensure your trades only occur within your specified hours, and the webhook alerts will notify you of trade executions. Strategy
Risk Reward Optimiser [ChartPrime]█ CONCEPTS
In modern day strategy optimization there are few options when it comes to optimizing a risk reward ratio. Users frequently need to experiment and go through countless permutations in order to tweak, adjust and find optimal in their data.
Therefore we have created the Risk Reward Optimizer.
The Risk Reward Optimizer is a technical tool designed to provide traders with comprehensive insights into their trading strategies.
It offers a range of features and functionalities aimed at enhancing traders' decision-making process.
With a focus on comprehensive data, it is there to help traders quickly and efficiently locate Risk Reward optimums for inbuilt of custom strategies.
█ Internal and external Signals:
The script can optimize risk to reward ratio for any type of signals
You can utilize the following :
🔸Internal signals ➞ We have included a number of common indicators into the optimizer such as:
▫️ Aroon
▫️ AO (Awesome Oscillator)
▫️ RSI (Relative Strength Index)
▫️ MACD (Moving Average Convergence Divergence)
▫️ SuperTrend
▫️ Stochastic RSI
▫️ Stochastic
▫️ Moving averages
All these indicators have 3 conditions to generate signals :
Crossover
High Than
Less Than
🔸External signal
▫️ by incorporating your own indicators into the analysis. This flexibility enables you to tailor your strategy to your preferences.
◽️ How to link your signal with the optimizer:
In order to be able to analysis your signal we need to read it and to do so we would need to PLOT your signal with a defined value
plot( YOUR LONG Condition ? 100 : 0 , display = display.data_window)
█ Customizable Risk to Reward Ratios:
This tool allows you to test seven different customizable risk to reward ratios , helping you determine the most suitable risk-reward balance for your trading strategy. This data-driven approach takes the guesswork out of setting stop-loss and take-profit levels.
█ Comprehensive Data Analysis:
The tool provides a table displaying key metrics, including:
Total trades
Wins
Losses
Profit factor
Win rate
Profit and loss (PNL)
This data is essential for refining your trading strategy.
🔸 It includes a tooltip for each risk to reward ratio which gives data for the:
Most Profitable Trade USD value
Most Profitable Trade % value
Most Profitable Trade Bar Index
Most Profitable Trade Time (When it occurred)
Position and size is adjustable
█ Visual insights with histograms:
Visualize your trading performance with histograms displaying each risk to reward ratio trade space, showing total trades, wins, losses, and the ratio of profitable trades.
This visual representation helps you understand the strengths and weaknesses of your strategy.
It offers tooltips for each RR ratio with the average win and loss percentages for further analysis.
█ Dynamic Highlighting:
A drop-down menu allows you to highlight the maximum values of critical metrics such as:
Profit factor
Win rate
PNL
for quick identification of successful setups.
█ Stop Loss Flexibility:
You can adjust stop-loss levels using three different calculation methods:
ATR
Pivot
VWAP
This allows you to align risk-reward ratios with your preferred risk tolerance.
█ Chart Integration:
Visualize your trades directly on your price chart, with each trade displayed in a distinct color for easy tracking.
When your take-profit (TP) level is reached , the tool labels the corresponding risk-reward ratio for that specific TP, simplifying trade management.
█ Detailed Tooltips:
Tooltips provide deeper insights into your trading performance. They include information about the most profitable trade, such as the time it occurred, the bar index, and the percentage gain. Histogram tooltips also offer average win and loss percentages for further analysis.
█ Settings:
█ Code:
In summary, the Risk Reward Optimizer is a data-driven tool that offers traders the ability to optimize their risk-reward ratios, refine their strategies, and gain a deeper understanding of their trading performance. Whether you're a day trader, swing trader, or investor, this tool can help you make informed decisions and improve your trading outcomes. Indicator
Strategy
[MT] Strategy Backtest Template| Initial Release | | EN |
An update of my old script, this script is designed so that it can be used as a template for all those traders who want to save time when programming their strategy and backtesting it, having functions already programmed that in normal development would take you more time to program, with this template you can simply add your favorite indicator and thus be able to take advantage of all the functions that this template has.
🔴Stop Loss and 🟢Take Profit:
No need to mention that it is a Stop Loss and a Take Profit, within these functions we find the options of: fixed percentage (%), fixed price ($), ATR, especially for Stop Loss we find the Pivot Points, in addition to this, the price range between the entry and the Stop Loss can be converted into a trailing stop loss, instead, especially for the Take Profit we have an option to choose a 1:X ratio that complements very well with the Pivot Points.
📈Heikin Ashi Based Entries:
Heikin Ashi entries are trades that are calculated based on Heikin Ashi candles but their price is executed to Japanese candles, thus avoiding false results that occur in Heikin candlestick charts, this making in certain cases better results in strategies that are executed with this option compared to Japanese candlesticks.
📊Dashboard:
A more visual and organized way to see the results and necessary data produced by our strategy, among them we can see the dates between which our operations are made regardless if you have activated some time filter, usual data such as Profit, Win Rate, Profit factor are also displayed in this panel, additionally data such as the total number of operations, how many were gains and how many losses, the average profit and loss for each operation and finally the maximum profits and losses followed, which are data that will be very useful to us when we elaborate our strategies.
Feel free to use this template to program your own strategies, if you find errors or want to request a new feature let me know in the comments or through my social networks found in my pulsewire profile.
| Update 1.1 | | EN |
➕Additions: '
Time sessions filter and days of the week filter added to the time filter section.
Option to add leverage to the strategy.
5 Moving Averages, RSI, Stochastic RSI, ADX, and Parabolic Sar have been added as indicators for the strategy.
You can choose from the 6 available indicators the way to trade, entry alert or entry filter.
Added the option of ATR for Take Profit.
Ticker information and timeframe are now displayed on the dashboard.
Added display customization and color customization of indicator plots.
Added customization of display and color plots of trades displayed on chart.
📝Changes:
Now when activating the time filter it is optional to add a start or end date and time, being able to only add a start date or only an end date.
Operation plots have been changed from plot() to line creation with line.new().
Indicator plots can now be controlled from the "plots" section.
Acceptable and deniable range of profit, winrate and profit factor can now be chosen from the "plots" section to be displayed on the dashboard.
Aesthetic changes in the section separations within the settings section and within the code itself.
The function that made the indicators give inputs based on heikin ashi candles has been changed, see the code for more information.
⚙️Fixes:
Dashboard label now projects correctly on all timeframes including custom timeframes.
Removed unnecessary lines and variables to take up less code space.
All code in general has been optimized to avoid the use of variables, unnecessary lines and avoid unnecessary calculations, freeing up space to declare more variables and be able to use fewer lines of code.
| Lanzamiento Inicial | | ES |
Una actualización de mi antiguo script, este script está diseñado para que pueda ser usado como una plantilla para todos aquellos traders que quieran ahorrar tiempo al programar su estrategia y hacer un backtesting de ella, teniendo funciones ya programadas que en el desarrollo normal te tomaría más tiempo programar, con esta plantilla puedes simplemente agregar tu indicador favorito y así poder aprovechar todas las funciones que tiene esta plantilla.
🔴Stop Loss y 🟢Take Profit:
No hace falta mencionar que es un Stop Loss y un Take Profit, dentro de estas funciones encontramos las opciones de: porcentaje fijo (%), precio fijo ($), ATR, en especial para Stop Loss encontramos los Pivot Points, adicionalmente a esto, el rango de precio entre la entrada y el Stop Loss se puede convertir en un trailing stop loss, en cambio, especialmente para el Take Profit tenemos una opción para elegir un ratio 1:X que se complementa muy bien con los Pivot Points.
📈Entradas Basadas en Heikin Ashi:
Las entradas Heikin Ashi son operaciones que son calculados en base a las velas Heikin Ashi pero su precio esta ejecutado a velas japonesas, evitando así́ los falsos resultados que se producen en graficas de velas Heikin, esto haciendo que en ciertos casos se obtengan mejores resultados en las estrategias que son ejecutadas con esta opción en comparación con las velas japonesas.
📊Panel de Control:
Una manera más visual y organizada de ver los resultados y datos necesarios producidos por nuestra estrategia, entre ellos podemos ver las fechas entre las que se hacen nuestras operaciones independientemente si se tiene activado algún filtro de tiempo, datos usuales como el Profit, Win Rate, Profit factor también son mostrados en este panel, adicionalmente se agregaron datos como el número total de operaciones, cuantos fueron ganancias y cuantos perdidas, el promedio de ganancias y pérdidas por cada operación y por ultimo las máximas ganancias y pérdidas seguidas, que son datos que nos serán muy útiles al elaborar nuestras estrategias.
Siéntete libre de usar esta plantilla para programar tus propias estrategias, si encuentras errores o quieres solicitar una nueva función házmelo saber en los comentarios o a través de mis redes sociales que se encuentran en mi perfil de pulsewire.
| Actualización 1.1 | | ES |
➕Añadidos:
Filtro de sesiones de tiempo y filtro de días de la semana agregados al apartado de filtro de tiempo.
Opción para agregar apalancamiento a la estrategia.
5 Moving Averages, RSI, Stochastic RSI, ADX, y Parabolic Sar se han agregado como indicadores para la estrategia.
Puedes escoger entre los 6 indicadores disponibles la forma de operar, alerta de entrada o filtro de entrada.
Añadido la opción de ATR para Take Profit.
La información del ticker y la temporalidad ahora se muestran en el dashboard.
Añadido personalización de visualización y color de los plots de indicadores.
Añadido personalización de visualización y color de los plots de operaciones mostradas en grafica.
📝Cambios:
Ahora al activar el filtro de tiempo es opcional añadir una fecha y hora de inicio o fin, pudiendo únicamente agregar una fecha de inicio o solamente una fecha de fin.
Los plots de operaciones han cambiados de plot() a creación de líneas con line.new().
Los plots de indicadores ahora se pueden controlar desde el apartado "plots".
Ahora se puede elegir el rango aceptable y negable de profit, winrate y profit factor desde el apartado "plots" para mostrarse en el dashboard.
Cambios estéticos en las separaciones de secciones dentro del apartado de configuraciones y dentro del propio código.
Se ha cambiado la función que hacía que los indicadores dieran entradas en base a velas heikin ashi, mire el código para más información.
⚙️Arreglos:
El dashboard label ahora se proyecta correctamente en todas las temporalidades incluyendo las temporalidades personalizadas.
Se han eliminado líneas y variables innecesarias para ocupar menos espacio en el código.
Se ha optimizado todo el código en general para evitar el uso de variables, líneas innecesarias y evitar los cálculos innecesarios, liberando espacio para declarar más variables y poder utilizar menos líneas de código.
Strategy
Template Trailing Strategy (Backtester)💭 Overview
+ Title: Template Trailing Strategy (Backtester)
+ Author: Iason Nikolas (jason5480)
+ License: CC BY-NC-SA 4.0
💢 What is the "Template Trailing Strategy (Backtester)" ❓
The "Template Trailing Strategy (Backtester)" (TTS) is a back-tester orchestration framework. It supercharges the implementation-test-evaluation lifecycle of new trading strategies, by making it possible to plug in your own trading idea.
While TTS offers a vast number of configuration settings, it primarily allows the trader to:
Test and evaluate your own trading logic that is described in terms of entry, exit, and cancellation conditions.
Define the entry and exit order types as well as their target prices when the limit, stop, or stop-limit order types are used.
Utilize a variety of options regarding the placement of the stop-loss and take-profit target(s) prices and support for well-known techniques like moving to breakeven and trailing.
Provide well-known quantity calculation methods to properly handle risk management and easily evaluate trading strategies and compare them.
Alert on each trading event or any related change through a robust and fully customizable messaging system.
All of the above makes TTS a practical toolkit: once you learn it, many repetitive tasks that strategy authors usually re-implement are eliminated. Using PulseWire’s built-in backtesting engine makes testing and comparing ideas straightforward.
By utilizing the TTS one can easily swap "trading logic" by testing, evaluating, and comparing each trading idea and/or individual component of a strategy.
Finally, TTS, through its per-event alert management (and debugging) system, provides an automated solution that supports live trading with brokers via webhooks.
NOTE: The "Template Trailing Strategy (Backtester)" does not dictate how you can combine different indicator types. Thus, it should not be confused as a "Trading System", because it gives its user full flexibility on that end (for better or worse).
💢 What is a "Signal Indicator" ❓
"Signal Indicator" (SI) is an indicator that can output a "signal" that follows a specific convention so that the "Template Trailing Strategy (Backtester)" can "understand" and execute the orders accordingly. The SI realizes the core trading logic signaling to the TTS when to enter, exit, or cancel an order. A SI instructs the TTS "when" to enter or exit, and the TTS determines "how" to enter and exit the position once the Signal Indicator generates a signal.
A very simple example of a Signal Indicator might be a 200-day Simple Moving Average Signal. When the price of the security closes above the 200-day SMA, a SI would provide TTS with a "long entry signal". Once TTS receives the "long entry signal", the TTS will open a long position and send an alert or automated trade message via webhook to a broker, based on the Entry settings defined in TTS. If the TTS Entry settings specify a "Market" order type, then the open long position will be executed by TTS immediately. But if the TTS Entry settings specify a "Stop" order type with a 1% Stop Distance, then when the price of the security rises by 1% after the "long entry signal" occurs, the TTS will open a long position and the Long Entry alert or webhook to the broker will be sent.
🤔 How to Guide
💢 How to connect a "signal" from a "Signal Indicator" ❓
The "Template Trailing Strategy (Backtester)" was designed to receive external signals from a "Signal Indicator". In this way, a "new trading idea" can be developed, configured, and evaluated separately from the TTS. Similarly, the SI can be held constant, and the trading mechanics can change in the TTS settings and back-tested to answer questions such as, "Am I better with a different stop loss placement method, what if I used a limit order instead of a stop order to enter, what if I used 25% margin instead of trading spot market?"
To make that possible by connecting an external signal indicator to TTS, you should:
Add both your SI (e.g. "Two MA Signal Indicator" , "Click Signal Indicator" , "Signal Adapter" , "Signal Composer" ) and the TTS script to the same chart.
Open the script's Settings / Inputs dialog for the TTS.
In the 🛠️ STRATEGY group set 𝐃𝐞𝐚𝐥 𝐂𝐨𝐧𝐝𝐢𝐨𝐧𝐬 𝐌𝐨𝐝𝐞 to 🔨External (this makes TTS listen to an external signal source).
Still inside 🛠️ STRATEGY locate the 🔌𝐒𝐢𝐠𝐧𝐚𝐥 🛈 input and choose the plotted output of your SI. The option should look like: "<SI short title>:🔌Signal to TTS" .
Verbose troubleshooting & tips
If the SI does not appear in the 🔌Signal 🛈 selector, confirm both scripts are added to the same chart and the SI exposes a plotted series (title often "🔌Signal to TTS").
When using multiple SIs, pick the SI instance that actually outputs the "🔌Signal to TTS" plotted series.
Validate on the chart: when your SI changes state, the plotted "🔌Signal" series in the TTS (visible in the data window) should change accordingly.
The TTS accepts only signals that follow the tts_convention DealConditions structure. Do not attempt to feed arbitrary scalar series without using conv.getDealConditions / conv.DealConditions.
Make sure your SI composes a DealConditions value following the TTS convention (startLong, endLong, startShort, endShort — optional cancel fields). See the template below.
If the plot is present but TTS does not react, ensure the SI plot is non-repainting (or accept realtime/backtest limitations). Test on historical bars first.
Create alerts on the strategy (see the Alerts section). Use the {{strategy.order.alert_message}} placeholder in the Create Alert dialog to forward TTS messages.
💢 How to create a custom trading logic ❓
The "Template Trailing Strategy (Backtester)" provides two ways to plug in your custom trading logic. Both of them have their advantages and disadvantages.
✍️ Develop your own Customized "Signal Indicator" 💥
The first approach is meant to be used for relatively more complex trading logic. The advantages of this approach are the full control and customization you have over the trading logic and the relatively simple configuration setup by having two scripts only. The downsides are that you have to have some experience with pinescript or you are willing to learn and experiment. You should also know the exact formula for every indicator you will use since you have to write it by yourself. Copy-pasting from existing open-source indicators will get you started quite fast though.
The idea here is either to create a new indicator script from scratch or to copy an existing non-signal indicator and make it a "Signal Indicator". To create a new script, press the "Pine Editor" button below the chart to open the "Pine Editor" and then press the "Open" button to open the drop-down menu with the templates. Select the "New Indicator" option. Add it to your chart to copy an existing indicator and press the source code {} button. Its source code will be shown in the "Pine Editor" with a warning on top stating that this is a read-only script. Press the "create a working copy". Now you can give a descriptive title and a short title to your script, and you can work on (or copy-paste) the (other) indicators of your interest. Once you have the information needed to decide, define a DealConditions object and plot it like this:
import jason5480/tts_convention/ as conv
// Calculate the start, end, cancel start, cancel end conditions
dealConditions = conv.DealConditions.new(
startLongDeal = ,
startShortDeal = ,
endLongDeal = ,
endShortDeal = ,
cnlStartLongDeal = ,
cnlStartShortDeal = ,
cnlEndLongDeal = ,
cnlEndShortDeal = )
// Use this signal in scripts like "Template Trailing Strategy (Backtester)" and "Signal Composer" that can utilize its value
// Emit the current signal value according to the TTS framework convention
plot(series = conv.getSignal(dealConditions), title = '🔌Signal to TTS', color = #808000, editable = false, display = display.data_window + display.status_line, precision = 0)
You should import the latest version of the tts_convention library and write your deal conditions appropriately based on your trading logic and put them in the code section shown above by replacing the "…" part after "=". You can omit the conditions that are not relevant to your logic. For example, if you use only market orders for entering and exiting your positions the cnlStartLongDeal, cnlStartShortDeal, cnlEndLongDeal, and cnlEndShortDeal are irrelevant to your case and can be safely omitted from the DealConditions object. After successfully compiling your new custom SI script add it to the same chart with the TTS by pressing the "Add to chart" button. If all goes well, you will be able to connect your "signal" to the TTS as described in the "How to connect a "signal" from a "Signal Indicator"?" guide.
🧩 Adapt and Combine existing non-signal indicators 💥
The second approach is meant to be used for relatively simple trading logic. The advantages of this approach are the lack of pine script and coding experience needed and the fact that it can be used with closed-source indicators as long as the decision-making part is displayed as a line in the chart. The drawback is that you have to have a subscription that supports the "indicator on indicator" feature so you can connect the output of one indicator as an input to another indicator. Please check if your plan supports that feature here
To plug in your own logic that way you have to add your indicator(s) of preference in the chart and then add the "Signal Adapter" script in the same chart as well. This script is a "Signal Indicator" that can be used as a proxy to define your custom logic in the CONDITIONS group of the "Settings/Inputs" tab after defining your inputs from your preferred indicators in the VARIABLES group. Then a "signal" will be produced, if your logic is simple enough it can be directly connected to the TTS that is also added to the same chart for execution. Check the "How to connect a "signal" from a "Signal Indicator"?" in the "🤔 How to Guide" for more information.
If your logic is slightly more complicated, you can add a second "Signal Adapter" in your chart. Then you should add the "Signal Composer" in the same chart, go to the SIGNALS group of the "Settings/Inputs" tab, and connect the "signals" from the "Signal Adapters". "Signal Composer" is also a SI so its composed "signal" can be connected to the TTS the same way it is described in the "How to connect a "signal" from a "Signal Indicator"?" guide.
At this point, due to the composability of the framework, you can add an arbitrary number (bounded by your subscription of course) of "Signal Adapters" and "Signal Composers" before connecting the final "signal" to the TTS.
💢 How to set up ⏰Alerts ❓
The "Template Trailing Strategy (Backtester)" provides a fully customizable per-event alert mechanism. This means that you may have an entirely different message for entering and exiting into a position, hitting a stop-loss or a take-profit target, changing trailing targets, etc. There are no restrictions, and this gives you great flexibility.
First enable the events you want under the "🔔 ALERT MESSAGES" module. Each enabled event exposes a text area where you can craft the message using placeholders that TTS replaces with actual values when the event occurs.
The placeholder categories (exact names used by the script) are:
Chart & instrument:
{{ticker}}
{{base_currency}}
{{quote_currency}}
Entry / exit / stop / TP prices & offsets:
{{entry_price}}
{{exit_price}}
{{stop_loss_price}}
{{take_profit_price_1}} ... {{take_profit_price_5}}
{{entry+_price}}, {{entry-_price}}, {{exit+_price}}, {{exit-_price}} — Optional offset helpers (computed using "Offset Ticks")
Quantities, percents & derived quantities:
{{entry_base_quantity}} — base units at entry (e.g. BTC)
{{entry_quote_quantity}} — quote amount at entry (e.g. USD)
{{risk_perc}} — % of capital risked for that entry (multiplied by 100 when "Percentage Range " is enabled)
{{remaining_quantity_perc}} — % of the initial position remaining at close/SL
{{remaining_base_quantity}} — remaining base units at close/SL
{{take_profit_quantity_perc_1}} ... {{take_profit_quantity_perc_5}} — % sold/bought at each TP
{{take_profit_base_quantity_1}} ... {{take_profit_base_quantity_5}} — base units closed at each TP
❗ Important: the per-event alert text is injected into the Create Alert dialog using PulseWire's strategy placeholder:
{{strategy.order.alert_message}}
During the creation of a strategy alert, make sure the placeholder {{strategy.order.alert_message}} exists in the "Message" box. PulseWire will substitute the per-event text you configured and enabled in TTS Settings/Inputs before sending it via webhook/notification.
Tip: For webhook/broker execution, set the proper "Condition" in the Create Alert dialog (for changing-entry/exit/SL notifications use "Order fills and alert() function calls" or "alert() function calls only" as appropriate).
💢 How to execute my orders in a broker ❓
To execute your orders in a broker that supports webhook integration, you should enable the appropriate alerts in the "Template Trailing Strategy (Backtester)" first (see the "How to set up Alerts?" guide above). Then you should go to the "Create Alert/Notifications" tab check the "Webhook URL" and paste the URL provided by your broker. You have to read the documentation of your broker for more information on what messages are expected.
Keep in mind that some brokers have deep integration with PulseWire so a per-event alert approach might be overkill.
📑 Definitions
This section tries to give some definitions in terms that appear in the "Settings/Inputs" tab of the "Template Trailing Strategy (Backtester)"
💢 What is Trailing ❓
Trailing is a technique where a price target follows another "barrier" price (usually high or low) by trying to keep a maximum distance from the "barrier" when it moves in only one direction (up or down). When the "barrier" moves in the other direction the price target will not change. There are as many types of trailing as price targets, which means that there are entry trailing, exit trailing, stop-loss trailing, and take-profit trailing techniques.
💢 What is a Moonbag ❓
A Moonbag in a trade is the quantity of the position that is reserved and will not be exited even if all take-profit targets defined in the strategy are hit, the quantity will be exited only if the stop-loss is hit or a close signal is received. This makes the stop-loss trailing technique in a trend-following strategy a good candidate to take advantage of a Moonbag.
💢 What is Distance ❓
Distance is the difference between two prices.
💢 What is Bias ❓
Bias is a psychological phenomenon where you make decisions based on market sentiment. For example, when you want to enter a long position you have a long bias, and when you want to exit from the long position you have a short bias. It is the other way around for the short position.
💢 What is the Bias Distance of a price target ❓
The Bias Distance of a price target is the distance that the target will deviate from its initial price. The direction of this deviation depends on the bias of the market. For example, suppose you are in a long position, and you set a take-profit target to the local highest high. In that case, adding a bias distance of five ticks will place your take-profit target 5 ticks below this local highest high because you have a short bias when exiting a long position. When the bias is long the bias distance will be added resulting in a higher target price and when you have a short bias the bias distance will be subtracted.
⚙️ Settings
In the "Settings/Inputs" tab of the "Template Trailing Strategy (Backtester)", you can find all the customizable settings that are provided by the framework. The variety of those settings is vast; hence we will only scratch the surface here. However, for every setting, there is an information icon 🛈 where you can learn more if you mouse over it. The "Settings/Inputs" tab is divided into ten main groups. Each one of them is responsible for one module of the framework. Every setting is part of a group that is named after the module it represents. So, to spot the module of a setting find the title that appears above it comes with an emoji and uppercase letters. Some settings might have the same name but belong to different modules e.g. "Tgt Dist Mtd" (Target Distance Method). Some settings are indented, which means that they are closely related to the non-indented setting above. Usually, indented settings provide further configuration for one or more options of the non-indented setting above. The groups that correspond to each module of the framework are the following:
🗺️ Quick Module Cross-Reference (use emojis to jump to setting groups)
📆 FILTERS — session, date & weekday filters
🛠️ STRATEGY — internal vs external deal-conditions; pick the signal source
🔧 STRATEGY – INTERNAL — built-in Two MA logic for demonstration purposes
🎢 VOLATILITY — ATR / StDev update modes
🔷 ENTRY — entry order types & trailing
🎯 TAKE PROFIT — multi-step TP and trailing rules
🛑 STOP LOSS — stop placement, move-to-breakeven, trailing
🟪 EXIT — exit order types & cancel logic
💰 QUANTITY/RISK MANAGEMENT — position sizing, moonbag, limits
📊 ANALYTICS — stats, streaks, seasonal tables
🔔 ALERT MESSAGES — per-event alert templates & placeholders
😲 Caveats
💢 Does "Template Trailing Strategy (Backtester)" have repainting behavior? ❓
The answer is that the "Template Trailing Strategy (Backtester)" does not repaint as long as the "Signal Indicator" that is connected also does not repaint. If you developed your own SI make sure that you understand and know how to prevent this behavior. The publication by @PineCoders here will give you a good idea on how to avoid most of the repainting cases.
⚠️ There is an exception though, when the "Enable Trail⚠️💹" checkbox is checked, the Take Profit trailing feature is enabled, and a tick-based approach is used, meaning that after a while, when the PulseWire discards all the real-time data, assumptions will be made by the backtesting engine that will cause a form of repainting. To avoid making false assumptions please disable this feature in the early stages and evaluate its usefulness in your strategy later on, after first confirming the success of the logic without this feature. In this case, consider turning on the bar magnifier feature. This way you will get more accurate backtest results when the Take Profit trailing feature is enabled.
💢 Can "Template Trailing Strategy (Backtester)" satisfy all my trading strategies ❓
While this framework can satisfy quite a large number of trading strategies there are cases where it cannot do so. For example, if you have a custom logic for your stop-loss or take-profit placement, or if you want to dollar cost average, then it might be better to start a new strategy script from scratch.
⚠️ It is not recommended to copy the official TTS code and start developing unless you are a Pine wizard! Even in that case, there is a stiff learning curve that might not be worth your time. Last, you must consider that I do not offer support for customized versions of the TTS script and if something goes wrong in the process you are all alone.
💝 Support & Feedback
For feedback, bug reports, or feature requests, contact me via PulseWire PM or use the script comments.
Note: The author's personal links and contact are available on the PulseWire profile.
🤗 Thanks
Special thanks to the welcoming community members, who regularly gave feedback all those years and helped me to shape the framework as it is today! Thanks everyone who contributed by either filing a "defect report" or asking questions that helped me to understand what improvements were necessary to help traders.
Enjoy!
Jason Strategy
Indicator
Ultimate Strategy TemplateHello Traders
As most of you know, I'm a member of the PineCoders community and I sometimes take freelance pine coding jobs for PulseWire users.
Off the top of my head, users often want to:
- convert an indicator into a strategy, so as to get the backtesting statistics from PulseWire
- add alerts to their indicator/strategy
- develop a generic strategy template which can be plugged into (almost) any indicator
My gift for the community today is my Ultimate Strategy Template
Step 1: Create your connector
Adapt your indicator with only 2 lines of code and then connect it to this strategy template.
For doing so:
1) Find in your indicator where are the conditions printing the long/buy and short/sell signals.
2) Create an additional plot as below
I'm giving an example with a Two moving averages cross.
Please replicate the same methodology for your indicator wether it's a MACD, ZigZag, Pivots, higher-highs, lower-lows or whatever indicator with clear buy and sell conditions
//@version=4
study(title='Moving Average Cross', shorttitle='Moving Average Cross', overlay=true, precision=6, max_labels_count=500, max_lines_count=500)
type_ma1 = input(title="MA1 type", defval="SMA", options= )
length_ma1 = input(10, title = " MA1 length", type=input.integer)
type_ma2 = input(title="MA2 type", defval="SMA", options= )
length_ma2 = input(100, title = " MA2 length", type=input.integer)
// MA
f_ma(smoothing, src, length) =>
iff(smoothing == "RMA", rma(src, length),
iff(smoothing == "SMA", sma(src, length),
iff(smoothing == "EMA", ema(src, length), src)))
MA1 = f_ma(type_ma1, close, length_ma1)
MA2 = f_ma(type_ma2, close, length_ma2)
// buy and sell conditions
buy = crossover(MA1, MA2)
sell = crossunder(MA1, MA2)
plot(MA1, color=color_ma1, title="Plot MA1", linewidth=3)
plot(MA2, color=color_ma2, title="Plot MA2", linewidth=3)
plotshape(buy, title='LONG SIGNAL', style=shape.circle, location=location.belowbar, color=color_ma1, size=size.normal)
plotshape(sell, title='SHORT SIGNAL', style=shape.circle, location=location.abovebar, color=color_ma2, size=size.normal)
/////////////////////////// SIGNAL FOR STRATEGY /////////////////////////
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Basically, I identified my buy, sell conditions in the code and added this at the bottom of my indicator code
Signal = buy ? 1 : sell ? -1 : 0
plot(Signal, title="🔌Connector🔌", transp=100)
Important Notes
🔥 The Strategy Template expects the value to be exactly 1 for the bullish signal , and -1 for the bearish signal
Now you can connect your indicator to the Strategy Template using the method below or that one
Step 2: Connect the connector
1) Add your updated indicator to a PulseWire chart
2) Add the Strategy Template as well to the SAME chart
3) Open the Strategy Template settings and in the Data Source field select your 🔌Connector🔌 (which comes from your indicator)
From then, you should start seeing the signals and plenty of other stuff on your chart
🔥 Note that whenever you'll update your indicator values, the strategy statistics and visual on your chart will update in real-time
Settings
- Color Candles : Color the candles based on the trade state (bullish, bearish, neutral)
- Close positions at market at the end of each session : useful for everything but cryptocurrencies
- Session time ranges : Take the signals from a starting time to an ending time
- Close Direction : Choose to close only the longs, shorts, or both
- Date Filter : Take the signals from a starting date to an ending date
- Set the maximum losing streak length with an input
- Set the maximum winning streak length with an input
- Set the maximum consecutive days with a loss
- Set the maximum drawdown (in % of strategy equity)
- Set the maximum intraday loss in percentage
- Limit the number of trades per day
- Limit the number of trades per week
- Stop-loss: None or Percentage or Trailing Stop Percentage or ATR
- Take-Profit: None or Percentage or ATR
- Risk-Reward based on ATR multiple for the Stop-Loss and Take-Profit
This script is open-source so feel free to use it, and optimize it as you want
Alerts
Maybe you didn't know it but alerts are available on strategy scripts.
I added them in this template - that's cool because:
- if you don't know how to code, now you can connect your indicator and get alerts
- you have now a cool template showing you how to create alerts for strategy scripts
Source: www.pulsewire.com
I hope you'll like it, use it, optimize it and most importantly....make some optimizations to your indicators thanks to this Strategy template
Special Thanks
Special thanks to @JosKodify as I borrowed a few risk management snippets from his website: kodify.net
Additional features
I thought of plenty of extra filters that I'll add later on this week on this strategy template
Best
Dave Strategy
Strategy
Strategy
Why is it ok to backtest on PulseWire from now on!PulseWire backtester has bad reputation. For a good reason - it was producing wrong results, and it was clear at first sight how bad they were.
But this has changed. Along with many other improvements in its PineScript coding capabilities, PulseWire fixed important bug, which was the main reason for miscalculations. PulseWire didn't really speak out about this fix, so let me try :)
Have a look at this short code of a swing trading strategy (PLEASE DON'T FOCUS ON BACKTEST RESULTS ATTACHED HERE - THEY DO NOT MATTER). Sometimes entry condition happens together with closing condition for the already ongoing trade. Example: the condition to close Long entry is the same as a condition to enter Short. And when these two aligned, not only a Long was closed and Short was entered (as intended), but also a second Short was entered, too!!! What's even worse, that second short was not controlled with closing conditions inside strategy.exit() function and it very often lead to losses exceeding whatever was declared in "loss=" parameter. This could not have worked well...
But HOORAY!!! - it has been fixed and won't happen anymore. So together with other improvements - PulseWire's backtester and PineScript is now ok to work with on standard candlesticks :)
Yep, no need to code strategies and backtest them on other platforms anymore.
----------------
Having said the above, there are still some pitfalls remaining, which you need to be aware of and avoid:
Don't backtest on HeikenAshi, Renko, Kagi candlesticks. They were not invented with backtesting in mind. There are still using wrong price levels for entries and therefore producing always too good backtesting results. Only standard candlesticks are reliable to backtest on.
Don't use Trailing Stop in your code. PulseWire operates only on closed candlesticks, not on tick data and because of that, backtester will always assume price has first reached its favourable extreme (so 'high' when you are in Long trade and 'low' when you are in Short trade) before it starts to pull back. Which is rarely the truth in reality. Therefore strategies using Trailing Stop are also producing too good backtesting results. It is especially well visible on higher timeframe strategies - for some reason your strategy manages to make gains on those huge, fat candlesticks :) But that's not reality.
"when=" inside strategy.exit() does not work as you would intuitively expect. If you want to have logical condition to close your trade (for example - crossover(rsi(close,14),20)) you need to place it inside strategy.close() function. And leave StopLoss + TakeProfit conditions inside strategy.exit() function. Just as in attached code.
If you're working with pyramiding, add "process_orders_on_close=ANY" to your strategy() script header. Default setting ("=FIFO") will first close the trade, which was opened first, not the one which was hit by Stop-Loss condidtion.
----------------
That's it, I guess :) If you are noticing other issues with backtester and would like to share, let everyone know in comments. If the issue is indeed a bug, there is a chance PulseWire dev team will hear your voice and take it into account when working on other improvements. Just like they heard about the bug I described above.
P.S. I know for a fact that more improvements in the backtesting area are coming. Some will change the game even for non-coding traders. If you want to be notified quickly and with my comment - gimme "follow". Strategy
Strategy
