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MA Multi-Factor Trend | Steel QuantMA Multi-Factor Trend is an ensemble-based trend system that synthesizes multiple technical dimensions into a unified directional signal. Rather than relying on a single metric, it requires confluence across momentum, trend structure, and price position before confirming bias — filtering noise and reducing false signals through multi-factor validation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔩 Core Philosophy Most approaches analyze one aspect of the market. MA Multi-Factor Trend takes a different path: it treats trend confirmation as a voting system. The primary MA establishes directional bias, but a signal only triggers when at least 3 of 4 independent filters agree. This consensus mechanism acts as a quality gate, ensuring entries align with multiple market dimensions simultaneously. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📐 Signal Architecture Primary Signal — Main MA The foundation layer. A configurable moving average (default: RMA 20) establishes baseline trend direction. Price above MA creates bullish bias, price below creates bearish bias. This serves as the directional gate that all filter signals must confirm. Filter 1 — RSI Momentum Measures the velocity of price movement. Bullish when RSI exceeds threshold (default: 60), indicating buying pressure supports the trend. Filters out entries during weak or exhausted momentum phases where reversals are more likely. Filter 2 — MACD Histogram Captures trend acceleration through the convergence/divergence of dual EMAs. Bullish when histogram > 0, confirming short-term momentum aligns with direction. Identifies trends gaining strength versus those losing steam. Filter 3 — Price vs Fast MA Validates price position relative to medium-term structure (default: SMA 50). Bullish when price trades above Fast MA. Ensures entries occur on the correct side of the trend, avoiding counter-trend positions. Filter 4 — MA Trend Structure Confirms the dominant market regime by comparing Fast MA to Slow MA (default: SMA 50 vs 200). Bullish when Fast MA > Slow MA. Keeps positions aligned with the broader trend environment and filters regime mismatches. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 Visual Components Chart Overlay — EMA Ribbon Dynamic ribbon structure that colors based on the combined signal output. White indicates confirmed bullish bias, black indicates confirmed bearish bias. Provides immediate visual feedback on trend state. Oscillator Panel — Filter Consensus Displays aggregate filter strength ranging from -100 (all bearish) to +100 (all bullish). The gradient fill visualizes conviction level — stronger readings indicate higher filter agreement. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ Configuration All parameters are fully adjustable to match different trading styles and market conditions: • Main MA: Method (RMA/EMA/SMA/WMA/VWMA/HMA), Length, Source • Trend MAs: Method, Fast Length, Slow Length • RSI: Length, Threshold • MACD: Fast Length, Slow Length, Signal Length • Visualization: Toggle chart overlay and oscillator panel independently ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 Use Cases • Trend Following — Enter when all factors align, ride until consensus breaks • Signal Filtering — Use as confirmation layer for other entry systems • Regime Detection — Oscillator panel reveals when market conviction shifts • Multi-Timeframe Analysis — Apply across timeframes to identify confluence zones ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or trading signals. Past performance is not indicative of future results. Trading financial instruments involves substantial risk, including the potential loss of your entire investment. Always conduct your own research and consult a qualified financial advisor before making any trading decisions. Use this tool at your own risk. The author assumes no responsibility for any losses incurred through its use.
Indicateur Pine Script®
par raphaelxsteel
Quantum X StrategyQuantum X Strategy — Expanded Description Quantum X Strategy is a carefully structured market-participation framework, designed to initiate trades only when strong directional alignment is detected across multiple independent market dimensions. Unlike reactive or single-indicator systems, this strategy evaluates the overall market context to ensure participation only occurs under conditions that have a higher probability of meaningful directional movement. Random or partial signals are ignored, with the system prioritizing structured, high-quality opportunities over frequency of trades. Structural Design The strategy’s decision-making process is based on a multi-dimensional analysis of price behavior: Directional Alignment: The system monitors multiple market behaviors to determine whether they collectively indicate bullish or bearish intent. Weighted Contribution: Each contributing factor is scored independently, and trades are considered only when the combined state reaches a meaningful threshold. Quality Filtering: The model filters out low-quality setups, minimizing the chance of entering trades in ambiguous or volatile conditions without sufficient confirmation. This design ensures that no single signal can trigger a trade independently, maintaining structural discipline and consistency in execution. Trade Dynamics Trade Activation: Trades are executed only when the internal alignment reaches a significant level of directional agreement. Sporadic or incomplete signals are ignored, ensuring that only setups with sufficient conviction are considered. Trade Closure: Positions are closed when the internal momentum alignment deteriorates or when a reversal in trend bias is detected. This dynamic exit approach prevents unnecessary exposure during weak market conditions. Market Inactivity: The system remains passive during periods of indecision, low volatility, or ambiguous market behavior. By staying inactive during such phases, the strategy reduces risk and avoids overtrading. Backtesting Context The strategy’s execution is restricted to post-2025 market data, ensuring that its performance reflects recent structural patterns and volatility behavior. Older market regimes, which may not be representative of current conditions, are intentionally excluded from analysis. This approach provides a realistic and relevant evaluation of the strategy’s effectiveness in today’s market environment. Intended Use Instrument: MIDCAPNIFTY Timeframe: 15-Minute Application: Suitable for intraday trading and short-term directional observation Risk Management: Designed to be used in conjunction with independent position sizing, stop-loss, and capital allocation discipline This system is most effective when traders maintain strict adherence to its entry and exit signals, avoiding discretionary overrides that could compromise the model’s integrity. Intellectual Property Notice The internal scoring methodology, alignment logic, and activation thresholds are intentionally abstracted to protect the originality and intellectual property of the strategy. The design prevents direct replication while still allowing traders and moderators to understand the conceptual framework behind its decisions. Disclaimer This strategy is provided strictly for educational, research, and backtesting purposes only. Market conditions evolve, and past performance does not guarantee future results. Traders are responsible for forward-testing and applying their own capital, risk, and position-sizing controls before implementing any live trades. 🔹 Moderator-Friendly Expanded Summary Instrument & Timeframe: MIDCAPNIFTY, 15-Minute Start Date: January 2025 onward Position Size: 1 lot / fixed quantity Initial Capital: ₹100,000 Commission & Slippage: 0.01% commission, 2-point slippage Trade Logic: Internal alignment model evaluating multiple independent market behaviors Trade Activation: Trades executed only when internal directional consensus reaches a significant threshold Trade Closure: Positions closed when alignment weakens or trend bias shifts Market Inactivity: System remains inactive during ambiguous, low-information, or low-volatility periods Risk Management: Users are encouraged to define stop-loss, capital allocation, and position-sizing according to personal risk tolerance IP Justification: Internal scoring, alignment logic, and thresholds are abstracted to maintain strategy originality Purpose: Strictly educational, research, and demonstration use only
Stratégie Pine Script®
par AlgoVisionX
Global Sovereign Spread MonitorIn the summer of 2011, the yield on Italian government bonds rose dramatically while German Bund yields fell to historic lows. This divergence, measured as the BTP-Bund spread, reached nearly 550 basis points in November of that year, signaling what would become the most severe test of the European monetary union since its inception. Portfolio managers who monitored this spread had days, sometimes weeks, of advance warning before equity markets crashed. Those who ignored it suffered significant losses. The Global Sovereign Spread Monitor is built on a simple but powerful observation that has been validated repeatedly in academic literature: sovereign bond spreads contain forward-looking information about systemic risk that is not fully reflected in equity prices (Longstaff et al., 2011). When investors demand higher yields to hold peripheral government debt relative to safe-haven bonds, they are expressing a view about credit risk, liquidity conditions, and the probability of systemic stress. This information, when properly analyzed, provides actionable signals for traders across all asset classes. The Science of Sovereign Spreads The academic study of government bond yield differentials began in earnest following the creation of the European Monetary Union. Codogno, Favero and Missale (2003) published what remains one of the foundational papers in this field, examining why yields on government bonds within a currency union should differ at all. Their analysis, published in Economic Policy, identified two primary drivers: credit risk and liquidity. Countries with higher debt-to-GDP ratios and weaker fiscal positions commanded higher yields, but importantly, these spreads widened dramatically during periods of market stress even when fundamentals had not changed significantly. This observation led to a crucial insight that Favero, Pagano and von Thadden (2010) explored in depth in the Journal of Financial and Quantitative Analysis. They found that liquidity effects can amplify credit risk during stress periods, creating a feedback loop where rising spreads reduce liquidity, which in turn pushes spreads even higher. This dynamic explains why sovereign spreads often move in non-linear fashion, remaining stable for extended periods before suddenly widening rapidly. Longstaff, Pan, Pedersen and Singleton (2011) extended this research in their American Economic Review paper by examining the relationship between sovereign credit default swap spreads and bond spreads across multiple countries. Their key finding was that a significant portion of sovereign credit risk is driven by global factors rather than country-specific fundamentals. This means that when spreads widen in Italy, it often reflects broader risk aversion that will eventually affect other asset classes including equities and corporate bonds. The practical implication of this research is clear: sovereign spreads function as a leading indicator for systemic risk. Aizenman, Hutchison and Jinjarak (2013) confirmed this in their analysis of European sovereign debt default probabilities, finding that spread movements preceded rating downgrades and provided earlier warning signals than traditional fundamental analysis. How the Indicator Works The Global Sovereign Spread Monitor translates these academic findings into a systematic framework for monitoring credit conditions. The indicator calculates yield differentials between peripheral government bonds and German Bunds, which serve as the benchmark safe-haven asset in European markets. Italian ten-year yields minus German ten-year yields produce the BTP-Bund spread, the single most important metric for Eurozone stress. Spanish yields minus German yields produce the Bonos-Bund spread, providing a secondary confirmation signal. The transatlantic US-Bund spread captures divergence between the two major safe-haven markets. Raw spreads are converted to Z-scores, which measure how many standard deviations the current spread is from its historical average over the lookback period. This normalization is essential because absolute spread levels vary over time with interest rate cycles and structural changes in sovereign debt markets. A spread of 150 basis points might have been concerning in 2007 but entirely normal in 2023 following the European debt crisis and subsequent ECB interventions. The composite index combines these individual Z-scores using weights that reflect the relative importance of each spread for global risk assessment. Italy receives the highest weight because it represents the third-largest sovereign bond market globally and any Italian debt crisis would have systemic implications for the entire Eurozone. Spain provides confirmation of peripheral stress, while the US-Bund spread captures flight-to-quality dynamics between the two primary safe-haven markets. Regime classification transforms the continuous Z-score into discrete states that correspond to different market environments. The Stress regime indicates that spreads have widened to levels historically associated with crisis periods. The Elevated regime signals rising risk aversion that warrants increased attention. Normal conditions represent typical spread behavior, while the Calm regime may actually signal complacency and potential mean-reversion opportunities. Retail Trader Applications For individual traders without access to institutional research teams, the Global Sovereign Spread Monitor provides a window into the macro environment that typically remains opaque. The most immediate application is risk management for equity positions. Consider a trader holding a diversified portfolio of European stocks. When the composite Z-score rises above 1.0 and enters the Elevated regime, historical data suggests an increased probability of equity market drawdowns in the coming days to weeks. This does not mean the trader must immediately liquidate all positions, but it does suggest reducing position sizes, tightening stop-losses, or adding hedges such as put options or inverse ETFs. The BTP-Bund spread specifically provides actionable information for anyone trading EUR/USD or European equity indices. Research by De Grauwe and Ji (2013) demonstrated that sovereign spreads and currency movements are closely linked during stress periods. When the BTP-Bund spread widens sharply, the Euro typically weakens against the Dollar as investors question the sustainability of the monetary union. A retail forex trader can use the indicator to time entries into EUR/USD short positions or to exit long positions before spread-driven selloffs occur. The regime classification system simplifies decision-making for traders who cannot constantly monitor multiple data feeds. When the dashboard displays Stress, it is time to adopt a defensive posture regardless of what individual stock charts might suggest. When it displays Calm, the trader knows that risk appetite is elevated across institutional markets, which typically supports equity prices but also means that any negative catalyst could trigger a sharp reversal. Mean-reversion signals provide opportunities for more active traders. When spreads reach extreme levels in either direction, they tend to revert toward their historical average. A Z-score above 2.0 that begins declining suggests professional investors are starting to buy peripheral debt again, which historically precedes broader risk-on behavior. A Z-score below minus 1.0 that starts rising may indicate that complacency is ending and risk-off positioning is beginning. The key for retail traders is to use the indicator as a filter rather than a primary signal generator. If technical analysis suggests a long entry in European stocks, check the sovereign spread regime first. If spreads are elevated or rising, the technical setup becomes higher risk. If spreads are stable or compressing, the technical signal has a higher probability of success. Professional Applications Institutional investors use sovereign spread analysis in more sophisticated ways that go beyond simple risk filtering. Systematic macro funds incorporate spread data into quantitative models that generate trading signals across multiple asset classes simultaneously. Portfolio managers at large asset allocators use sovereign spreads to make strategic allocation decisions. When the composite Z-score trends higher over several weeks, they reduce exposure to peripheral European equities and bonds while increasing allocations to German Bunds, US Treasuries, and other safe-haven assets. This rotation often happens before explicit risk-off signals appear in equity markets, giving these investors a performance advantage. Fixed income specialists at banks and hedge funds use sovereign spreads for relative value trades. When the BTP-Bund spread widens to historically elevated levels but fundamentals have not deteriorated proportionally, they may go long Italian government bonds and short German Bunds, betting on mean reversion. These trades require careful risk management because spreads can widen further before reversing, but when properly sized they offer attractive risk-adjusted returns. Risk managers at financial institutions use sovereign spread monitoring as an input to Value-at-Risk models and stress testing frameworks. Elevated spreads indicate higher correlation among risk assets, which means diversification benefits are reduced precisely when they are needed most. This information feeds into position sizing decisions across the entire trading book. Currency traders at proprietary trading firms incorporate sovereign spreads into their EUR/USD and EUR/CHF models. The relationship between the BTP-Bund spread and EUR weakness is well-documented in academic literature and provides a systematic edge when combined with other factors such as interest rate differentials and positioning data. Central bank watchers use sovereign spreads to anticipate policy responses. The European Central Bank has demonstrated repeatedly that it will intervene when spreads reach levels that threaten financial stability, most notably through the Outright Monetary Transactions program announced in 2012 and the Transmission Protection Instrument introduced in 2022. Understanding spread dynamics helps investors anticipate these interventions and position accordingly. Interpreting the Dashboard The statistics panel provides real-time information that supports both quick assessments and deeper analysis. The composite Z-score is the primary metric, representing the weighted average of all spread Z-scores. Values above zero indicate spreads are wider than their historical average, while values below zero indicate compression. The magnitude matters: a reading of 0.5 suggests modestly elevated stress, while 2.0 or higher indicates conditions similar to historical crisis periods. The regime classification translates the Z-score into actionable categories. Stress should trigger immediate review of risk exposure and consideration of hedges. Elevated warrants increased vigilance and potentially reduced position sizes. Normal indicates no immediate concerns from sovereign markets. Calm suggests risk appetite may be elevated, which supports risk assets but also creates potential for sharp reversals if sentiment changes. The percentile ranking provides historical context by showing where the current Z-score falls within its distribution over the lookback period. A reading of 90 percent means spreads are wider than they have been 90 percent of the time over the past year, which is significant even if the absolute Z-score is not extreme. This metric helps identify when spreads are creeping higher before they reach official stress thresholds. Momentum indicates whether spreads are widening or compressing. Rising momentum during elevated spread conditions is particularly concerning because it suggests stress is accelerating. Falling momentum during stress suggests the worst may be past and mean reversion could be beginning. Individual spread readings allow traders to identify which component is driving the composite signal. If the BTP-Bund spread is elevated but Bonos-Bund remains normal, the stress may be Italy-specific rather than systemic. If all spreads are widening together, the signal reflects broader flight-to-quality that affects all risk assets. The bias indicator provides a simple summary for traders who need quick guidance. Risk-Off means spreads indicate defensive positioning is appropriate. Risk-On means spread conditions support risk-taking. Neutral means spreads provide no clear directional signal. Limitations and Risk Factors No indicator provides perfect signals, and sovereign spread analysis has specific limitations that users must understand. The European Central Bank has demonstrated its willingness to intervene in sovereign bond markets when spreads threaten financial stability. The Transmission Protection Instrument announced in 2022 specifically targets situations where spreads widen beyond levels justified by fundamentals. This creates a floor under peripheral bond prices and means that extremely elevated spreads may not persist as long as historical patterns would suggest. Political events can cause sudden spread movements that are impossible to anticipate. Elections, government formation crises, and policy announcements can move spreads by 50 basis points or more in a single session. The indicator will reflect these moves but cannot predict them. Liquidity conditions in sovereign bond markets can temporarily distort spread readings, particularly around quarter-end and year-end when banks adjust their balance sheets. These technical factors can cause spread widening or compression that does not reflect fundamental credit risk. The relationship between sovereign spreads and other asset classes is not constant over time. During some periods, spread movements lead equity moves by several days. During others, both markets move simultaneously. The indicator provides valuable information about credit conditions, but users should not expect mechanical relationships between spread signals and subsequent price moves in other markets. Conclusion The Global Sovereign Spread Monitor represents a systematic application of academic research on sovereign credit risk to practical trading decisions. The indicator monitors yield differentials between peripheral and safe-haven government bonds, normalizes these spreads using statistical methods, and classifies market conditions into regimes that correspond to different risk environments. For retail traders, the indicator provides risk management information that was previously available only to institutional investors with access to Bloomberg terminals and dedicated research teams. By checking the sovereign spread regime before executing trades, individual investors can avoid taking excessive risk during periods of elevated credit stress. For professional investors, the indicator offers a standardized framework for monitoring sovereign credit conditions that can be integrated into broader macro models and risk management systems. The real-time calculation of Z-scores, regime classifications, and component spreads provides the inputs needed for systematic trading strategies. The academic foundation is robust, built on peer-reviewed research published in top finance and economics journals over the past two decades. The practical applications have been validated through multiple market cycles including the European debt crisis of 2011-2012, the COVID-19 shock of 2020, and the rate normalization stress of 2022. Sovereign spreads will continue to provide valuable forward-looking information about systemic risk for as long as credit conditions vary across countries and investors respond rationally to changes in default probabilities. The Global Sovereign Spread Monitor makes this information accessible and actionable for traders at all levels of sophistication. References Aizenman, J., Hutchison, M. and Jinjarak, Y. (2013) What is the Risk of European Sovereign Debt Defaults? Fiscal Space, CDS Spreads and Market Pricing of Risk. Journal of International Money and Finance, 34, pp. 37-59. Codogno, L., Favero, C. and Missale, A. (2003) Yield Spreads on EMU Government Bonds. Economic Policy, 18(37), pp. 503-532. De Grauwe, P. and Ji, Y. (2013) Self-Fulfilling Crises in the Eurozone: An Empirical Test. Journal of International Money and Finance, 34, pp. 15-36. Favero, C., Pagano, M. and von Thadden, E.L. (2010) How Does Liquidity Affect Government Bond Yields? Journal of Financial and Quantitative Analysis, 45(1), pp. 107-134. Longstaff, F.A., Pan, J., Pedersen, L.H. and Singleton, K.J. (2011) How Sovereign Is Sovereign Credit Risk? American Economic Review, 101(6), pp. 2191-2212. Manganelli, S. and Wolswijk, G. (2009) What Drives Spreads in the Euro Area Government Bond Market? Economic Policy, 24(58), pp. 191-240. Arghyrou, M.G. and Kontonikas, A. (2012) The EMU Sovereign-Debt Crisis: Fundamentals, Expectations and Contagion. Journal of International Financial Markets, Institutions and Money, 22(4), pp. 658-677.
Indicateur Pine Script®
par EdgeTools
Sesion Operativa - Codigo InstitucionalThis indicator is designed for institutional and precision traders who need to visualize market liquidity and key session operating ranges without visual clutter. Unlike standard session indicators, this tool focuses on clarity and the projection of key levels (Highs and Lows) to identify potential future reaction zones. Key Features: 4 Customizable Sessions: Pre-configured with key institutional times (Pre-NY, NY Open, London, and Asia). Each session is fully adjustable in time, color, and style. Minimalist Labeling: Displays the session name and operating range (in pips/points) in a clean, direct format (e.g., NY - 45), removing decimals and unnecessary text to keep the chart clean. Range Projections: Option to project the Highs and Lows of each session forward (N candles) to use them as dynamic support or resistance levels. Opening Highlight (NYSE): Special feature to highlight candle colors during specific high-volatility times (default 09:30 - 09:35 UTC-5), perfect for identifying manipulation or liquidity injections at the stock market open. Adjustable Time Zone: Default setting is UTC-5 (New York), but fully adaptable to any user time zone.
Indicateur Pine Script®
par sebaztiansaenzX
BiasFlow Long System🔹 Short summary “BiasFlow Long System” is an invite-only, long-only strategy designed to participate in bullish trends using a combination of: • a directional “bias” filter based on price behaviour over time, and • candle-structure conditions that confirm short-term strength before entering, plus a simple risk-management layer (stop loss and optional take profit). The system is intentionally selective: it aims to enter only when a clear upward bias and a cluster of bullish price action align, and then to exit on opposite conditions or risk-based levels. It is NOT a holy grail and NOT financial advice. ──────────────────────── 0. Legal / risk disclaimer ──────────────────────── • This script is invite-only and for EDUCATIONAL and RESEARCH purposes only. • It is NOT financial advice and does NOT guarantee profits. • Backtest results can differ significantly from live trading results. • Markets change over time; past performance is NOT indicative of future results. • You are fully responsible for your own trading decisions and risk. Do not trade with money you cannot afford to lose. Always start with demo / paper trading and make sure you understand how the strategy behaves on your own market and timeframe before risking real capital. ──────────────────────── 1. About default settings and risk (very important) ──────────────────────── The internal `strategy()` call uses: • `initial_capital = 100` → This is only a simple example account size for testing. • `default_qty_type = strategy.percent_of_equity` • `default_qty_value = 100` → This means 100% of equity per trade in the default properties. → This is EXTREMELY AGGRESSIVE and should be treated purely as a STRESS TEST of the logic, **not** as a realistic way to trade. To align with TradingView’s Strategy Results guidelines and more realistic risk management, you should: 1. Open **Strategy Settings → Properties**. 2. Change: • Order size type → **Percent of equity** (if not already). • Order size (percent) → e.g. **1–2%** per trade (or any small risk that fits your plan). 3. Check that **commission & slippage** are realistic for your broker and market. • The script uses a 0.1% example commission and a small slippage value as a starting point, but you must adapt them to your conditions. If you decide to run 100% of equity per trade, treat it only as a stress scenario for backtesting the behaviour of the system, **never** as a recommended risk profile for live trading. ──────────────────────── 2. What this strategy tries to do (conceptual overview) ──────────────────────── BiasFlow Long System is a **long-only, bias-based trend participation strategy**. Conceptually, it tries to: 1. Detect when the market has a **sustained upward directional bias** using an internal bias filter applied directly to price behaviour over time. 2. Wait for a **short-term cluster of bullish candles** in that favourable environment before entering a long position. 3. Use **risk-based exits** (stop loss and optional take profit) together with a bearish candle-structure condition to close trades when the upward bias fails or local conditions deteriorate. In other words, it is not trying to catch every small fluctuation. Instead, it waits for the market to **lean upward** and then demands a clear, short-term confirmation from the candles before committing capital, exiting either on a controlled risk level or on a structured bearish pattern. ──────────────────────── 3. Components and how they work together ──────────────────────── BiasFlow Long System consists of three main building blocks: (1) Time / backtest window control • You can select a continuous start/end date range. • You can also use a **year-selector** (checkboxes per year) to include or exclude specific calendar years. • This allows you to: - test the strategy across long histories, - compare behaviour in different regimes (e.g. 2018 vs 2021), - avoid accidentally cherry-picking a tiny, overly-optimistic window. (2) Bias engine • Internally, the strategy computes a **directional bias** from price. • It classifies the environment into broad states like “up”, “down” (and internally handles flat conditions). • Long entries are only allowed when the bias engine deems the environment favourable (an “up” state). • This prevents the strategy from buying blindly into obvious downtrends. (3) Candle-structure and risk module • Entry signals require a **cluster of bullish candles** that meet strict internal conditions. - Exact rules are deliberately not disclosed, but the idea is to demand multiple aligned bullish bars to confirm local strength before entering. • Exits can be triggered by: - a **cluster of bearish candles** under suitable conditions, signalling local weakness, and/or - the risk module (stop loss / take profit) if those levels are hit first. These components are designed to work together so that the strategy only participates when: • the broader environment supports longs (bias engine), and • the immediate price action confirms that bullish pressure is actually present (candle structure), while exits are handled in a rule-based way either by candle structure or by pre-defined risk thresholds. ──────────────────────── 4. Entry & Exit logic (high level) ──────────────────────── At a high conceptual level: A) Time filter • Only bars inside your chosen backtest window (date range or selected years) are considered for entries and exits. • This helps you analyse specific periods (e.g. only post-2020 data) without changing the code. B) Entry (long-only) A long trade is considered only when all of the following conceptual conditions are met: 1. The bar is inside the allowed backtest window. 2. The **bias engine** classifies the environment as favourable for longs (up-bias). 3. The most recent candles form a **bullish sequence** according to internal rules (e.g. price closing strongly vs. open on several consecutive bars). If these conditions align, the strategy opens a **single long position** with the sizing defined in your Strategy Properties (for example 1–2% of equity per trade). C) Risk-based exit Once in a position, the strategy maintains a basic risk framework: • **Stop Loss (SL)**: - Defined as a percentage distance below the average entry price. - Enabled by default in the Inputs, but you can adjust the percentage or disable it if you want to test raw logic. • **Take Profit (TP)**: - Also defined as a percentage distance above the average entry price. - By default, the TP module is optional and configured as a very wide level so it does not interfere unless you intentionally enable and tune it. - You should set a realistic TP (for example a multiple of your risk) if you want to use it. The SL/TP orders are managed as OCO exits by TradingView, so if one is hit first, the other is cancelled automatically. D) Candle-based exit In addition to risk exits: • The strategy watches for a **structured bearish sequence** of candles while the bias is still acceptable for exits. • When that bearish structure appears, the strategy closes the open long position. • This allows the system to respond to a change in short-term price behaviour even if the stop loss or take profit have not been reached yet. ──────────────────────── 5. Recommended backtest configuration (to avoid misleading results) ──────────────────────── To keep your results in line with TradingView’s Strategy Results guidelines and avoid misleading curves: 1. **Initial capital** - You can keep 100 as in the code or choose any other realistic account size. 2. **Order size (RISK PER TRADE)** - Type: **Percent of equity**. - Recommended: **1–2% per trade** as a starting point. - Avoid using more than 5–10% risk per trade if you want something that could be sustainable in real trading. 3. **Commission & slippage** - Commission: for example 0.1% if that approximates your broker’s fee. - Slippage: a few ticks (e.g. 3) to represent real fills. - Always adjust these to your instrument and broker conditions. 4. **Timeframe & markets** - The system is designed to work on trending instruments (for example major crypto pairs or indices). - Typical timeframes: 1D is reasonable starting points but you can try with 1H / 4H. - On higher timeframes, trades will be rarer but may aim at larger swings. 5. **Avoid “caution warning” backtests** - If TradingView shows warnings like “too few trades” or “insufficient data” in your chosen configuration, consider: - expanding the backtest period, - switching to a more liquid / volatile instrument, or - changing timeframe to produce a more meaningful sample. ──────────────────────── 5a. About low trade count and selective signals ──────────────────────── BiasFlow Long System is **not** a high-frequency scalping algorithm. It is deliberately selective: • It is long-only. • It requires a favourable bias environment AND a specific pattern of bullish candles before entering. • On higher timeframes (e.g. Daily) or very strict filter settings, the strategy can produce a **relatively low number of trades** over many years of data. TradingView often recommends having 100+ trades for stronger statistics. In this particular system: • A lower trade count is a **conscious design choice**, reflecting the goal of focusing on a smaller set of higher-conviction long setups rather than constant trading. • Because of this, backtest metrics (profit factor, win rate, etc.) should NOT be interpreted as statistically “proven” – they are just one sample of how this logic would have behaved on past data. Always use caution when drawing conclusions from a small number of trades. ──────────────────────── 6. How to use this strategy (step-by-step) ──────────────────────── 1. **Add the script to your chart** - Choose your instrument and timeframe (e.g. BTCUSDT 1D, or any trending symbol you want to study). 2. **Configure the backtest window** - In the Inputs, set either: - a specific Start Date (e.g. 2018-01-01), or - use the year checkboxes to include/exclude calendar years. - This allows you to test different regimes (pre-/post-halving, bull vs. bear, etc.). 3. **Adjust risk settings** - Open Inputs → Risk Management: - Choose whether to use the Stop Loss and/or Take Profit. - Set realistic percentages for your market and volatility. - Open Strategy Properties: - Set order size to a realistic % of equity (e.g. 1–2%). - Verify commission and slippage. 4. **Run the backtest** - Inspect: - Net Profit, Max Drawdown, Profit Factor - Number of trades and average trade duration - Equity curve shape (smooth vs. choppy). 5. **Experiment carefully** - Try different symbols, timeframes, and risk settings. - Observe how the system behaves in different market regimes and how sensitive it is to your parameter choices. 6. **Forward-test in demo** - Before even considering live usage, run the system on a paper account and watch how signals appear in real time. - Make sure the behaviour matches your expectations from the backtest. ──────────────────────── 7. Originality and usefulness (why this is more than a mashup) ──────────────────────── BiasFlow Long System is not just a visual mashup of common indicators on a chart. It is a **coherent, bias-driven framework** with: • A dedicated **time / regime control** (year and date filters) to study behaviour across multiple cycles. • An internal **bias engine** that only allows trades when the market structure supports longs. • A **candle-structure layer** that requires a sequence of aligned bullish or bearish bars, rather than isolated single-bar signals. • A simple but practical **risk module** that integrates percentage-based SL/TP exits. The core logic is intentionally abstracted and not publicly disclosed, but the conceptual design is: • to combine directional bias, • with short-term confirmation, • under explicit risk-management constraints, in a way that is testable, repeatable, and suitable as a base for further private research and improvement. ──────────────────────── 8. Limitations and good practices ──────────────────────── • No strategy performs well in all markets and all conditions. • This system is **long-only**, so in prolonged bear markets it may spend long periods out of the market or perform poorly. • Performance is sensitive to: - timeframe, - instrument volatility, - risk settings (SL/TP, position size). Good practices: • Test on multiple instruments and timeframes. • Focus on drawdowns, stability, and robustness, not just on maximum profit. • Avoid overfitting by constantly re-optimising parameters to your last backtest window. • Treat this as a **framework and research tool**, not a plug-and-play money printer. ──────────────────────── 9. Licensing and credits ──────────────────────── • Code and logic: - “BiasFlow Long System” created by Jokiniemi Marcin Arcisz. • This script is invite-only. • If you reuse or extend ideas from this system, please do so in a way that respects TradingView’s House Rules and the author’s intent. ──────────────────────── 10. Invite-only / vendor information ──────────────────────── • This strategy is distributed on an **invite-only** basis. • There is **no guarantee of profit** and no claim that this strategy will outperform the market. • The description focuses on the conceptual design and risk considerations so that TradingView users and moderators can understand what it tries to do and how to use it responsibly. • Any access, subscription, or collaboration outside TradingView, if it exists, should always comply with TradingView’s Vendor Requirements and general House Rules. ──────────────────────── 11. Example backtest settings used in screenshots ──────────────────────── To avoid confusion about how example results were produced, here is one concrete configuration you can use as a starting point: • Symbol: BTCUSDT (or another major, liquid trending pair) • Timeframe: 1D • Backtest period: from 2018-01-01 to the most recent available data • Initial capital: 100 • Order size type: Percent of equity • Order size: 2% per trade • Commission: 0.1% • Slippage: 3 ticks • Risk settings: - Stop Loss enabled with a moderate % distance from entry - Take Profit disabled or set to a realistic multiple of the risk • Filters: - Backtest window: multiple years selected - Bias engine and candle-structure logic enabled (as they are part of the core system) If you change any of these parameters (symbol, timeframe, risk per trade, commission, slippage, backtest window, etc.), your results will look different. Always adapt the configuration to your own risk tolerance, market, and trading style.
Stratégie Pine Script®
par jokiniemi
Discipline Sleeping TimeThe Sleeping Time indicator highlights a predefined time window on the chart that represents your sleeping hours. This will help doing backtest easily by filtering out unrealistic result of trades while we are still sleeping. During the selected period: - The chart background is softly shaded to visually mark your sleep window - The first candle of the range is labeled “Sleep” - The last candle of the range is labeled “Wake Up” You can also use it for other purpose. This makes it easy to: - Visually avoid trading during sleep hours - Identify when a trading session should be inactive - Maintain discipline and consistency across different markets and timezones Key Features: - Custom Time Range Define your sleeping hours using a start and end time. - UTC Offset Selector Adjust the time window using a UTC offset dropdown (−10 to +13), so the indicator aligns correctly with your local time. - Clear Visual Markers Background shading during sleep hours - Start label: Sleep - End label: Wake Up - Customizable Labels Change label text, size, and style to suit your chart layout. Best Use Case Use this indicator to lock in rest time, avoid emotional trades, and respect personal trading boundaries. Because good trades start with good sleep 😴
Indicateur Pine Script®
par LightNCI
Strategy H4-H1-M15 Triple Screen + TableMaster of Multi-Timeframe Trading: "Triple Screen" Strategy "▲▼ & BUY/SELL M15 Tags" — H1 Ready signals warn the trader in advance that a reversal is brewing on the medium timeframe. Settings: Stochastic Settings: Oscillator length and smoothing adjustment. Overbought/Oversold: Overbought/oversold level settings (default 80/20). SL Offset: Buffer in ticks/pips for setting stop-loss beyond extremes. Usage Instructions: Long: Background painted light green (H4 Trend UP + H1 Stoch Low), wait for green "BUY M15" tag. Short: Background painted light red (H4 Trend DOWN + H1 Stoch High), wait for red "SELL M15" tag. Entry → SL → TP = PROFIT Short Description (for preview): Comprehensive "Triple Screen" strategy based on MACD (H4) and Stochastic (H1, M15). Features trend monitoring panel and precise entry signals with automatic Stop Loss calculation. Technical Notes (for developers): Hardcoded Timeframes: "240" (H4) and "60" (H1) are hardcoded. For universal use on other timeframe combinations (D1-H4-H1), make these input.timeframe variables. Repainting: request.security may cause repainting on historical bars (current bar is honest). Standard practice for multi-timeframe TradingView indicators. Alerts: Built-in alert support for one-click trading convenience.
Indicateur Pine Script®
par Alex8181999
1
Seasonality AdvancedDescription This is a professional-grade Seasonality Analysis tool designed to project future price trends based on historical cyclical patterns. Unlike simple seasonal indicators that just average price, this script offers a statistical approach with a "Zero Gravity" visualization mode and a real-time Data Dashboard. Underlying Concepts & Methodology The script calculates the seasonal tendency by averaging the price performance of the same day/week over a user-defined lookback period (e.g., 5, 10, or 15 years). Data Alignment: It aligns historical data based on trading days (default 252) or calendar days to create a coherent "Annual Cycle". Smoothing: A Moving Average is applied to the raw seasonal data to filter out noise and reveal the true macro tendency. Correlation Engine: It calculates the real-time correlation between the current price action and the projected seasonal line. This acts as a "Lie Detector"—if correlation is high, the seasonal pattern is currently valid. Key Features Multi-Cycle Analysis: Plot up to 3 different seasonal baselines simultaneously (e.g., Short-term 5Y vs. Long-term 15Y cycles). Zero Gravity View: Uses a "Joyplot" style separation (Stacking) to prevent lines from overlapping messily, making it easier to compare different cycles. Statistical Dashboard: A built-in table displays Avg Return, WinRate, Volatility Risk, and Correlation. How to Use Projections: Use the lines extending into the future to anticipate potential turning points. Confluence: When the 5-year and 10-year lines point in the same direction, the probability increases. Filter: Watch the "Correlation" column in the table. Low correlation means the current market is decoupling from history. To comply with House Rules regarding non-English UI, here is the translation of the script's settings menu: 1. Cálculos Sazo 3 (Calculation Settings) Dias de negociação = Trading Days (Fixed 252 or Variable) Método de dias = Day Count Method (Min, Max, Avg) Projeção Futura (Barras) = Future Projection (Bars) Suavização (Média) = Smoothing (MA Length) Deslocamento = Offset 2. Visualização e Layout (Visuals) Empilhamento / Separação (%) = Stacking / Separation % Distância Vertical = Vertical Distance Distância da Etiqueta = Label Offset 3. Painel Estatístico (Statistics Panel) Mostrar Tabela = Show Table Mostrar Próximo Mês = Show Next Month Mostrar Linha Méd/Alvo = Show Avg/Target Row Texto Suave = Soft Text (Transparency) Período Correlação = Correlation Period Tema = Theme (Dark/Light) Tamanho = Size Posição = Position 4. Linha de Hoje (Today's Line) Mostrar Linha = Show Vertical Line Cor/Estilo/Espessura = Color/Style/Width 5. Linhas 1, 2, 3 (Seasonal Lines) Ativar Linha = Enable Line Período (anos) = Lookback Period (Years) Descrição Este indicador é uma ferramenta completa de Sazonalidade que projeta tendências futuras baseadas em padrões históricos. Ele inclui um painel estatístico exclusivo que mostra a probabilidade (WinRate) e a correlação do ciclo atual. Funcionalidades Projeção Futura: Desenha o comportamento provável do preço para os próximos dias. Painel Estatístico: Mostra retorno médio, risco e correlação em tempo real. Zero Gravity: Visualização empilhada para facilitar a leitura de múltiplos ciclos.
Indicateur Pine Script®
par marcaoemail_1
STOC Trend Analysis for F&O For Long Term trend Analysis. I have added three STs for long term investments. This indicator absorbs the short term volatility. //Follow me on Twitter @STOC_Master// This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or trade signals. The creator and Systematic Traders Club are not responsible for any financial losses resulting from the use of this indicator. Trading and investing involve risk. Always do your own analysis and use proper risk management.
Indicateur Pine Script®
par BoobeshDasarath93
Pro Minimalist ATR (Black)The script I provided is a tool that automatically calculates and displays volatility "zones" around the average price. Here is the plain English explanation of what it is doing and why: 1. The Anchor: 20 DMA (The "Fair Value") The script starts by calculating the 20-Day Moving Average (20 DMA). What it represents: Think of this as the "fair price" or the "center of gravity" for the market over the last month. In the script: It looks at the closing price of the last 20 candles, adds them up, and divides by 20. This is your baseline. 2. The Ruler: ATR (The "Volatility") Next, it measures the Average True Range (ATR) over the last 14 days. What it represents: This measures the "energy" or "noise" of the market. If candles are huge, the ATR is high. If candles are tiny, the ATR is low. Why we use it: Using a fixed number (like $50) doesn't work because stocks move differently. ATR adapts to the current market mood. 3. The Zones: +1, +2, -1, -2 The script then takes that "center" (20 DMA) and adds/subtracts the "ruler" (ATR) to create four distinct levels: +1 ATR: This is the "Upper Normal" limit. Price hanging here is bullish but normal. +2 ATR: This is the "Extreme" limit. Statistically, price rarely stays above this line for long without snapping back. This is often an overbought signal. -1 ATR: This is the "Lower Normal" limit. -2 ATR: This is the "Extreme" discount. If price hits this, it is statistically stretched far below its average. 4. The Visuals: "Clean" Labeling Finally, the script focuses on presentation: No Lines: It specifically avoids drawing lines all over your history to keep your chart clean. Dynamic Labels: It creates text labels only on the very last bar (the current moment). It constantly deletes the old label and draws a new one as the price moves, so it looks like the text is "floating" next to the current price. Axis Marking: It forces marks onto the right-hand price scale (display=display.price_scale) so you can see the exact price levels (e.g., 154.20) without having to guess.
Indicateur Pine Script®
par GoyalKapil354
(Fri RangeCore Function: Weekly Friday Range - Captures the entire Friday's trading range (from midnight to midnight NY time) Extended Display - Shows that range for 6 more days (through next Thursday) 25%/75% Levels - Adds support/resistance levels at the 25% and 75% points of the Friday range Swing Detection - When price breaks out of the Friday range, it finds and marks the most recent swing point that led to that breakout Key Visual Elements: DR Range (Daily Range): Friday's high and low IDR Range (Inside Day Range): Friday's opening range (between open and close) Opening Line: Friday's opening price 25%/75% Lines: Support/resistance levels within the range Swing Lines: When breakout occurs, draws horizontal lines from the swing point that preceded it Why It's Useful: 1. Weekly Context Shows how Friday's trading activity sets up the following week Provides a weekly "anchor point" for traders to reference 2. Breakout Analysis When price breaks above/below Friday's range, it shows where the move started from (the swing point) Helps identify if breakouts are genuine or false 3. Support/Resistance Levels The 25%/75% levels act as natural support/resistance within the range These often become targets or reversal points 4. Multi-Timeframe Perspective Combines daily (Friday), weekly (range extension), and swing (violation) analysis Shows how short-term swings relate to weekly ranges 5. Trading Applications: Range Trading: Trade bounces between 25%/75% levels and range extremes Breakout Trading: Enter when price breaks the range, target the swing level Reversal Trading: Fade moves at swing levels after range violations Market Structure: See how weekly ranges contain or fail to contain price action
Indicateur Pine Script®
par shariff09z