Mulai

Cari skrip untuk "btc走势预测"

makemoney-hybridSo this model is little different from moneymaker model in the following : The buy system based on super trend , the sell system =S is based on the volume model of money maker in the example we set 7% take profit for both long or short . you can set it even higher since btc very volatile now in cases where it did not reach the target its made min of 3% each direction So the buy in this system will be in true uptrend . since now the btc is falling more then going up we can make more money on shorts and wait for the longs when they come :) the bullish and bear zone based on super system you need to set correctly your take profit in order to make it to work . the more volatile will be the coin the better will be the results (this is the theory )
Indikator Pine Script®
oleh RafaelZioni
5
RSI / Stoch / SRSI / MFI / Aroon Overlay [SigmaDraconis]Combines 4 popular indicators (RSI, Stoch, SRSI, MFI) and 1 peculiar one (Aroon) in 1 for those who want to save indicators but not only. This is an evolution of my (simpler) "RSI / Stoch / Stoch RSI (SRSI) Overlay " that you can find on my scripts. Added bands for oversold/overbought areas (70/30 common for RSI and 80/20 for SRSI and MFI), as well as a middle 50 horizontal line. Neutral bands around 55-45 added as well that can be hidden for less clutter. I also recommend a more transparent coloring for these since Pine script doesn't allow default transparency for horizontal lines. By default only RSI and Stoch are activated, you can activate Aroon, MFI and SRSI on the inputs window. Some extra notes: * RSI, Stoch and MFI can help to strengthen one's decision as well as Aroon to predict a possible trend reversal, SRSI can show when RSI has high probability of being topped or bottomed when oversold/overbought but don't forget to look at volume and how the trend progresses that can keep SRSI above 80 or below 20 while RSI and price continues to trend, divergences are most helpful here to find possible reversal areas. * This chart depicts some interesting divergences, as well as Stoch tops and bottoms and confluences between RSI/MFI and Stoch on some over-extended tops and bottoms that shown being good reversal zones. RSI resistances are shown as well, failing to break above 60 or the neutral zone (this is a bearish BTC trend chart after all) or failing to gain support to break up certain levels (RSI notes a more bullish trend when consistently above 60 and more bearish below 40). If you like it and use it to profit, please tip me below :) Tip jars: BTC: 15nMBiEGVrdGcu9C1h6QRcTNRvugHkqrMQ ETH: 0xC33845946c48B61fBCbEA0367ec2238CaF2b73bc BTS: sigma-draconis
Indikator Pine Script®
oleh SigmaDraconis
37
Relative Estimated Price REP by KIVANÇ fr3762Relative Estimated Price (REP) Indicator shows the estimated price calculated if the tickerid made the same value changes (in %) during a certain period. The default value of the lookback period is 50. In the given XRPUSD chart you can see that XRPUSD has a value of 0.26480 and the RPC indicator shows the value of 0.38099. This means that XRP would be 0.38099USD if it was fully made the same percentage moves with BTC , we can say that XRP is RELATIVELY cheap according to BTC price moves. Conversely XRP would be RELATIVELY expensive if the last value of REP was lower then current XRP price. users can choose the relative base price in calculation of REP between 1-5 which are: 1=BTCUSD, 2=ETHUSD, 3=EURTRY(Euro/Turkish Lira), 4=USDTRY (Dollar/Turkish Lira), 5=BIST100 (Istanbul Stock Exchange) I personally advise you to use this indicator for daily charts in Tradingview to have more accurate estimated prices because of the website's calculation. Developed by KIVANÇ
Indikator Pine Script®
oleh KivancOzbilgic
7
[NG] Indicator - Altcoin Alpha - v1(Created for Client) Alpha (Unique price action of asset) indicator for ALTcoins implementation, taking `BINANCE:BTCUSDT` as the market reference. Can be improved by adding more BTC charts from more sources, so as to get a unified chart of BTC for market representation. Set `alpha period` to a value, wherein you want to see the unique price action of the asset. For short term trend, a value of 24 is good for `1H` charts (1 day), and value of 168 is good for long term trends on `1H` charts (1 week trend). Corresponding values of `beta period` should be `168` (1 week for 1 day alpha) and `720` (1 month for 1 week alpha period). You can set `alpha` and `beta` period as per your requirements. Regards,
Indikator Pine Script®
oleh NikhilGuptab4
Noro's Trend MAs Strategy v1.8Trade strategy which uses only 2 MA. The slow MA (blue) is used for definition of a trend The fast MA (red) is used for an entrance to the transaction For: - For H1 - For crypto/fiat or crypto/crypto - Good for "BTC/USD", "ETH/USD", "ETH/BTC" Recomended: Long = true (if it is profitable as a result of backtests) Short = true (if it is profitable as a result of backtests) Stops = false Stop, % = any OHLC4 = any Use Fast MA = true Fast MA Period = 5 Slow MA Period = 21 Bars Q = (2 for "bitcoin/fiat" or 1 for "crypto/fiat" or 0 for "crypto/crypto") In the new version 1.8 - The second PriceChannel is added - Profit became more - Losses became less - The unnecessary types of MA are removed
Strategi Pine Script®
oleh ROBO_Trading
Bitcoin momentum correlation This is a pretty simple indicator, it measures the momentum of bitcoin as compared to usd,eur,eth,dash, and ltc, which you can see in all of the blue lines. If the red line is above zero then it means the overall value of btc is going up, opposite for down. The Ema_window controls how smooth the signal is. If you shorten the Ema_window parameter and open this on higher timeframe btc charts then the zero crossing gives pretty solid signals, despite being pretty choppy. A good way to interpret this is that if all the blue lines are moving in the same direction at once without disagreement, then the value of bitcoin has good momentum. Mildly more technically: Momentum is measured in the first derivative of an EMA for each ticker. To normalize the different values against each other they are all divided by their local maximums, which can be chosen in the parameter window, but shouldn't make a huge difference. All the checked values are then summed, as shown in the red line. To include a value into the red line simply keep it checked. Take a look at the script, it's kind of easy on the eyes. It's pretty handy to look at, but doesn't seem too worthwhile to pursue much further. If someone wants much more out of the script then feel free to message me. Remember rules #1 & #2 Don't lose money. Happy trading
Indikator Pine Script®
oleh mcbw_
3
Volume Conversion IndicatorVolume Conversion Indicator The volume conversion indicator is much like the in-built volume indicator. This particular volume indicator allows you to find out how much of something has been traded in a given timeframe. This is done by multiplying volume by the average price at that point. What does this mean? Well, say, for example, you were watching DGB/BTC (DigiByte/Bitcoin). Instead of the volume being displayed in the amount of DGB traded, the amount of BTC traded is displayed instead. Feel free to comment... Hope this helps :D
Indikator Pine Script®
oleh SquishyPea
Indicator: Schaff Trend Cycle (STC)Another new indicator for TV community :) STC detects up and down trends long before the MACD. It does this by using the same exponential moving averages (EMAs), but adds a cycle component to factor instrument cycle trends. STC gives more accuracy and reliability than the MACD. More info: www.investopedia.com Feel free to "Make mine" this chart and use the indicator in your charts. Appreciate any feedback on how effective this is for your instrument (I have tested this only with BTC). For people trading BTC: ------------------------------- Try 3/10 or 9/30 for MACD (fastLength/slowLength). They seem to catch the cycles better than the defaults. :)
Indikator Pine Script®
oleh LazyBear
33
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion . Core Architecture Why These Components Are Combined: The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention. The Four Trading Agents 1. Spoofing Detector Agent 🎭 Detects iceberg orders through persistent volume at similar price levels over 5 bars Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average) Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model) Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation Best Markets: Consolidations, institutional trading windows, low-liquidity hours 2. Exhaustion Detector Agent ⚡ Calculates RSI divergence between price movement and momentum indicator over 5-bar window Detects VWAP exhaustion (price at 2σ bands with declining volume) Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure Signal Logic: Counter-trend—enters when momentum extreme shows weakness Best Markets: Trending markets reaching climax points, over-extended moves 3. Liquidity Void Detector Agent 💧 Measures Bollinger Band squeeze (width <60% of 50-period average) Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR) Signal Logic: Breakout anticipation—enters after liquidity grab but before main move Best Markets: Range-bound pre-breakout, volatility compression zones 4. Mean Reversion Agent 📊 Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ) Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model) Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability) Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments Adaptive Selection: Multi-Armed Bandit System The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance: Thompson Sampling (Default - Recommended): Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent) Balances exploration (trying underused agents) vs. exploitation (using proven winners) Each agent's win/loss history informs its selection probability Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation UCB1 (Upper Confidence Bound): Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls) Deterministic algorithm favoring agents with high uncertainty (potential upside) More conservative than Thompson Sampling Epsilon-Greedy: Exploits best-performing agent (1-ε)% of the time Explores randomly ε% of the time (default 10%, configurable 1-50%) Simple, transparent, easily tuned via epsilon parameter Gradient Bandit: Uses softmax probability distribution over agent preference weights Updates weights via gradient ascent based on rewards Best for Blend mode where all agents contribute Selection Modes: Switch Mode: Uses only the selected agent's signal (clean, decisive) Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified) Lock Agent Feature: Optional manual override to force one specific agent Useful after identifying which agent dominates your specific instrument Only applies in Switch mode Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion Memory System Dual-Layer Architecture: Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50) Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars) Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20% Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds) How Memory Works: Agent generates signal → outcome tracked after 8 bars (performance horizon) Result stored in short-term memory (win = 1.0, loss = 0.0) Short-term average influences agent's future scores (positive feedback loop) After threshold met (default 10 results), episode averaged into long-term storage Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost Market Microstructure Analysis These advanced metrics quantify institutional order flow dynamics: Order Flow Toxicity (Simplified VPIN): Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol) Detects informed trading activity (institutional players with non-public information) Values >0.4 indicate "toxic flow" (informed traders active) Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification Price Impact Analysis (Simplified Kyle's Lambda): Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10) Low values = large orders with minimal price impact ( stealth accumulation ) High values = retail-dominated moves with high slippage Lite Approximation: Uses simplified denominator instead of regression-based signed order flow Market Randomness (Entropy Analysis): Counts unique price changes over 20 bars / 20 Measures market predictability High entropy (>0.6) = human-driven, chaotic price action Low entropy (<0.25) = algorithmic trading dominance (predictable patterns) Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x)) Order Clustering (Simplified Hawkes Process): Tracks self-exciting event intensity (coordinated order activity) Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average High intensity (>0.7) indicates clustering (potential spoofing/accumulation) Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE Signal Generation Process Multi-Stage Validation: Stage 1: Agent Scoring Each agent calculates internal score based on its detection criteria Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier) Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0) Stage 2: Memory Boost Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance) Successful agents get amplified, failing agents get dampened Stage 3: Bandit Selection/Blending If Adaptive Mode ON: Switch: Bandit selects single best agent, uses only its signal Blend: All agents combined using softmax-weighted confidence scores If Adaptive Mode OFF: Traditional consensus voting with confidence-squared weighting Signal fires when consensus exceeds threshold (default 70%) Stage 4: Confirmation Filter Raw signal must repeat for consecutive bars (default 3, configurable 2-4) Minimum confidence threshold: 0.25 (25%) enforced regardless of mode Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2 Stage 5: Cooldown Enforcement Minimum bars between signals (default 10, configurable 5-15) Prevents over-trading during choppy conditions Stage 6: Performance Tracking After 8 bars (performance horizon), signal outcome evaluated Win = price moved in signal direction, Loss = price moved against Results fed back into memory and bandit statistics Trading Modes (Presets) Pre-configured parameter sets: Conservative: 85% consensus, 4 confirmations, 15-bar cooldown Expected: 60-70% win rate, 3-8 signals/week Best for: Swing trading, capital preservation, beginners Balanced: 70% consensus, 3 confirmations, 10-bar cooldown Expected: 55-65% win rate, 8-15 signals/week Best for: Day trading, most traders, general use Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown Expected: 50-58% win rate, 15-30 signals/week Best for: Scalping, high-frequency trading, active management Elite: 75% consensus, 3 confirmations, 12-bar cooldown Expected: 58-68% win rate, 5-12 signals/week Best for: Selective trading, high-conviction setups Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown Expected: Varies based on learning Best for: Experienced users leveraging bandit system How to Use 1. Initial Setup (5 Minutes): Select Trading Mode matching your style (start with Balanced) Enable Adaptive Learning (recommended for automatic agent selection) Choose Thompson Sampling algorithm (best all-around performance) Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume) 2. Agent Tuning (Optional): Adjust Agent Sensitivity multipliers (0.5-2.0): <0.8 = Highly selective (fewer signals, higher quality) 0.9-1.2 = Balanced (recommended starting point) 1.3 = Aggressive (more signals, lower individual quality) Monitor dashboard for 20-30 signals to identify dominant agent If one agent consistently outperforms, consider using Lock Agent feature 3. Bandit Configuration (Advanced): Blend Temperature (0.1-2.0): 0.3 = Sharp decisions (best agent dominates) 0.5 = Balanced (default) 1.0+ = Smooth (equal weighting, democratic) Memory Decay (0.8-0.99): 0.90 = Fast adaptation (volatile markets) 0.95 = Balanced (most instruments) 0.97+ = Long memory (stable trends) 4. Signal Interpretation: Green triangle (▲): Long signal confirmed Red triangle (▼): Short signal confirmed Dashboard shows: Active agent (highlighted row with ► marker) Win rate per agent (green >60%, yellow 40-60%, red <40%) Confidence bars (█████ = maximum confidence) Memory size (short-term buffer count) Colored zones display: Entry level (current close) Stop-loss (1.5× ATR) Take-profit 1 (2.0× ATR) Take-profit 2 (3.5× ATR) 5. Risk Management: Never risk >1-2% per signal (use ATR-based stops) Signals are entry triggers, not complete strategies Combine with your own market context analysis Consider fundamental catalysts and news events Use "Confirming" status to prepare entries (not to enter early) 6. Memory Persistence (Optional): After 50-100 trades, check Memory Export Panel Record displayed alpha/beta/weight values for each agent Record VPIN and Kyle threshold values Enable "Restore From Memory" and input saved values to continue learning Useful when switching timeframes or restarting indicator Visual Components On-Chart Elements: Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend) Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers) Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR) Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal) Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets Signal Markers: Triangle up/down at validated entry points Dashboard (Configurable Position & Size): Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear) Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus) Agent Performance Table: Real-time win%, confidence, and memory stats Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled) Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress Memory Panel (Configurable Position & Size): Live Parameter Export: Alpha, beta, and weight values per agent Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold Save Reminder: Visual indicator if parameters should be recorded What Makes This Original This script's originality lies in three key innovations: 1. Genuine Meta-Learning Framework: Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning. 2. Episodic Memory Architecture with Transfer Learning: The dual-layer memory system mimics human learning patterns: Short-term memory captures recent performance (recency bias) Long-term memory preserves historical patterns (experience) Automatic transfer mechanism consolidates knowledge Memory boost creates positive feedback loops (successful strategies become stronger) This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates. 3. Institutional Microstructure Integration: Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis. Mashup Justification: The four agents are combined specifically for risk diversification across failure modes: Spoofing Detector: Prevents false breakout losses from manipulation Exhaustion Detector: Prevents chasing extended trends into reversals Liquidity Void: Exploits volatility compression (different regime than trending) Mean Reversion: Provides mathematical anchoring when patterns fail The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals. Why "ML-lite"? Simplifications and Approximations This is the "lite" version due to necessary simplifications for Pine Script execution: 1. Simplified VPIN Calculation: Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume) Impact: May misclassify volume during ranging/choppy markets; works best in directional moves 2. Pseudo-Random Sampling: Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100 Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection 3. Hawkes Process Approximation: Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event) Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments 4. Kyle's Lambda Simplification: Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals 5. Entropy Calculation: Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude This Implementation: Simple ratio of unique price changes to total observations (variety measure) Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape 6. Memory System Constraints: Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking 7. Limited Feature Engineering: Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations 8. Single-Instrument Data: Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes This Implementation: Only OHLCV data from displayed instrument Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets 9. Fixed Performance Horizon: Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement This Implementation: Fixed 8-bar evaluation window Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach Performance Impact Summary: These simplifications make the script: ✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations ✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers ✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models) ✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint ⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations ⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows ⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized When to Upgrade to Full Implementation: Consider professional Python/C++ versions with institutional data feeds if: Trading with >$100K capital where precision differences materially impact returns Operating in microsecond-competitive environments (HFT, market making) Requiring regulatory-grade audit trails and reproducibility Backtesting with tick-level precision for strategy validation Need true real-time adaptation with neural network-based learning For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact. Technical Notes All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap ) VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above) Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling No repainting: All calculations use confirmed bar data (no forward-looking) Maximum lookback: 500 bars (set via max_bars_back parameter) Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed) Confidence threshold: Minimum 0.25 (25%) enforced on all signals Memory arrays: Dynamic sizing with FIFO queue management Limitations and Disclaimers Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty. Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context. Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms. Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts. Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets. No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss. Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics. Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes. Recommended Settings Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning) Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices) Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable) Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens) Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0) After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent Alert Configuration The script includes built-in alert conditions: Long Signal: Fires when validated long entry confirmed Short Signal: Fires when validated short entry confirmed Alerts fire once per bar (after confirmation requirements met) Set alert to "Once Per Bar Close" for reliability Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Indikator Pine Script®
oleh DskyzInvestments
2
Range Oscillator Strategy + Stoch Confirm / Long Only by (Mara)Strategy Overview This script is a long-only trend-following strategy that combines: – Range-based trend filter (inspired by "Range Oscillator" by Zeiierman) – Stochastic (7 / 3 / 3) for pullback & entry timing – EMA Exit Filter for adaptive exits as default 70 Use it to backtest, tweak, and study how this combination behaves on different markets. 1. Trend Filter Trade longs only when the trend filter value is above the user-defined Threshold. If the filter ≤ Threshold → no new long entries. Threshold is adjustable in the settings. 2. Pullback Logic – Stochastic (7 / 3 / 3) While trend is bullish: Wait for %K to pull back below a chosen level (e.g. 50 or 20), then look for a bullish %K over %D crossover. All parameters are configurable. 3. Entry Conditions (Long Only) – Trend filter > Threshold – Stoch pullback condition met – Bullish %K > %D at bar close No short entries. 4. Stop Loss & Take Profit Configurable Stop Loss and optional Take Profit. Use Inputs to compare fixed RR vs. indicator/exit-based management. 5. EMA Exit Filter (Optional) Additional custom exit logic to react faster to weakening conditions. Toggleable in Inputs. 6. How to Test Try on BTC and major markets (1D / 4D / 1H) and review: net profit, drawdown, win rate, and robustness over multiple years. 7. Disclaimer This script is for testing, education, and strategy development only. It is not financial advice and not a guarantee of profits. Always manage your own risk.
Strategi Pine Script®
oleh jokiniemi
Aquantprice: Institutional Structure MatrixSETUP GUIDE Open TradingView Go to Indicators Search: Aquantprice: Institutional Structure Matrix Click Add to Chart Customize: Min Buy = 10, Min Sell = 7 Show only PP, R1, S1, TC, BC Set Decimals = 5 (Forex) or 8 (Crypto) USE CASES & TRADING STRATEGIES 1. CPR Confluence Trading (Most Popular) Rule: Enter when ≥3 timeframes show Buy ≥10/15 or Sell ≥7/13 text Example: Daily: 12/15 Buy Weekly: 11/15 Buy Monthly: 10/15 Buy → **STRONG LONG BIAS** Enter on pullback to nearest **S1 or L3** 2. Hot Zone Scalping (Forex & Indices) Rule: Trade only when price is in Hot Zone (closest 2 levels) text Hot: S1-PP → Expect bounce or breakout Action: - Buy at S1 if Buy Count ↑ - Sell at PP if Sell Count ↑ 3. Institutional Reversal Setup Rule: Price at H3/L3 + Reversal Condition text Scenario: Price touches **Monthly L3** L3 in **Hot Zone** Buy Count = 13/15 → **High-Probability Reversal Long** 4. CPR Width Filter (Avoid Choppy Markets) Rule: Trade only if CPR Label = "Strong Trend" text CPR Size < 0.25 → Trending CPR Size > 0.75 → Sideways (Avoid) 5. Multi-Timeframe Bias Dashboard Use "Buy" and "Sell" columns as a sentiment meter TimeframeBuySellBiasDaily123BullishWeekly89BearishMonthly112Bullish → Wait for alignment before entering HOW TO READ THE TABLE Column Meaning Time frame D, W, M, 3M, 6M, 12MOpen Price Current session open PP, TC, BC, etc. Pivot levels (color-coded if in Hot Zone) Buy X/15 conditions met (≥10 = Strong Buy)Sell X/13 conditions met (≥7 = Strong Sell)CPR Size Histogram + Label (Trend vs Range)Zone Hot: PP-S1, Med: S2-L3, etc. + PP Distance PRO TIPS Best on 5M–1H charts for entries Use with volume or order flow for confirmation Set alerts on Buy ≥12/15 or Sell ≥10/13 Hide unused levels to reduce clutter Combine with AQuantPrice Dashboard (Small TF) for full system IDEAL MARKETS Forex (EURUSD, GBPUSD, USDJPY) Indices (NAS100, SPX500, DAX) Crypto (BTC, ETH – use 6–8 decimals) Commodities (Gold, Oil) 🚀 **NEW INDICATOR ALERT** **Aquantprice: Institutional Structure Matrix** The **ALL-IN-ONE CPR Dashboard** used by smart money traders. ✅ **6 Timeframes in 1 Table** (Daily → Yearly) ✅ **15 Buy + 13 Sell Conditions** (Institutional Logic) ✅ **Hot Zones, CPR Width, PP Distance** ✅ **Fully Customizable – Show/Hide Any Level** ✅ **Real-Time Zone Detection** (Hot, Med, Low) ✅ **Precision up to 8 Decimals** **No more switching charts. No more confusion.** See **where institutions are positioned** — instantly. 👉 **Add to Chart Now**: Search **"Aquantprice: Institutional Structure Matrix"** 🔥 **Free Access | Pro-Level Insights** *By AQuant – Trusted by 10,000+ Traders* #CPR #PivotTrading #SmartMoney #TradingView FINAL TAGLINE "See What Institutions See — Before They Move." Aquantprice: Institutional Structure Matrix Your Edge. One Dashboard.
Indikator Pine Script®
oleh Nmn0708
EMA Cross + RSI + ADX - Autotrade Strategy V2Overview A versatile trend-following strategy combining EMA 9/21 crossovers with RSI momentum filtering and optional ADX trend strength confirmation. Designed for both cryptocurrency and traditional futures/options markets with built-in stop loss management and automated position reversals. Key Features Multi-Market Compatibility: Works on both crypto futures (Bitcoin, Ethereum) and traditional markets (NIFTY, Bank NIFTY, S&P 500 futures, equity options) Triple Confirmation System: EMA crossover + RSI filter + ADX strength (optional) Automated Risk Management: 2% stop loss with wick-touch detection Position Auto-Reversal: Opposite signals automatically close and reverse positions Webhook Ready: Six distinct alert messages for automation (Entry Buy/Sell, Close Long/Short, SL Hit Long/Short) Performance Metrics NIFTY Futures (15min): 50%+ win rate with ADX filter OFF Crypto Markets: Requires extensive backtesting before live deployment Optimal Timeframes: 15-minute to 1-hour charts (patience required for higher timeframes) Strategy Logic Entry Signals: LONG: EMA 9 crosses above EMA 21 + RSI > 55 + ADX > 20 (if enabled) SHORT: EMA 9 crosses below EMA 21 + RSI < 45 + ADX > 20 (if enabled) Exit Signals: Opposite EMA crossover (auto-closes current position) Stop loss hit at 2% from entry price (tracks candle wicks) Technical Indicators: Fast EMA: 9-period (short-term trend) Slow EMA: 21-period (primary trend) RSI: 14-period with 55/45 thresholds (momentum confirmation) ADX: 14-period with 20 threshold (trend strength filter - optional) Market-Specific Settings Traditional Markets (NIFTY, Bank NIFTY, S&P Futures, Options) Recommended Settings: ADX Filter: Turn OFF (less choppy, cleaner trends) Timeframe: 15-minute chart Win Rate: 50%+ on NIFTY Futures Why No ADX: Traditional markets have more institutional participation and smoother price action, making ADX unnecessary Cryptocurrency Markets (BTC, ETH, Altcoins) Recommended Settings: ADX Filter: Turn ON (ADX > 20) Timeframe: 15-minute to 1-hour Extensive backtesting required before live trading Why ADX: Crypto markets are highly volatile and prone to false breakouts; ADX filters low-quality chop Best Practices ✅ Backtest thoroughly on your specific instrument and timeframe ✅ Use larger timeframes (1H, 4H) for higher quality signals and better risk/reward ✅ Adjust RSI thresholds based on market volatility (try 52/48 for more signals, 60/40 for fewer but stronger) ✅ Monitor ADX effectiveness - disable for traditional markets, enable for crypto ✅ Proper position sizing - adjust default_qty_value based on your capital and instrument price ✅ Paper trade first - test for 2-4 weeks before risking real capital Risk Management Fixed 2% stop loss per trade (adjustable) Stop loss tracks candle wicks for accurate execution Positions auto-reverse on opposite signals (no manual intervention needed) 0.075% commission built into backtest (adjust for your broker) Customization Options All parameters are adjustable via inputs: EMA periods (default: 9/21) RSI length and thresholds (default: 14-period, 55/45 levels) ADX length and threshold (default: 14-period, 20 threshold) Stop loss percentage (default: 2%) Webhook Automation This strategy includes six distinct alert messages for automated trading: "Entry Buy" - Long position opened "Entry Sell" - Short position opened "Close Long" - Long position closed on opposite crossover "Close Short" - Short position closed on opposite crossover "SL Hit Long" - Long stop loss triggered "SL Hit Short" - Short stop loss triggered Compatible with Delta Exchange, Binance Futures, 3Commas, Alertatron, and other webhook platforms. Important Notes ⚠️ Crypto markets require extensive backtesting - volatility patterns differ significantly from traditional markets ⚠️ Higher timeframes = better results - 15min works but 1H/4H provide cleaner signals ⚠️ ADX toggle is critical - OFF for traditional markets, ON for crypto ⚠️ Not financial advice - always conduct your own research and use proper risk management ⚠️ Past performance ≠ future results - backtest results may not reflect live trading conditions Disclaimer This strategy is for educational and informational purposes only. Trading futures and options involves substantial risk of loss. Always backtest thoroughly, start with paper trading, and never risk more than you can afford to lose. The author assumes no responsibility for any trading losses incurred using this strategy.
Strategi Pine Script®
oleh varuns_back
2
Amir Mohammad Lor QUANTUM SMC PRO ® – 2025 LAUNCH - Current time: November 10, 2025 09:17 AM CET - Country: DE ★★★ FREE FOR FIRST 5,000 USERS – THEN LOCKED FOREVER! ★★★ ● 100% NON-REPAINT ● 38 Order Block types + Smart FVG + Real-time Liquidity Sweep ● BOS / CHoCH / EQH / EQL / Mitigation / Imbalance / Grab ● LIVE 97.3% Win-Rate Dashboard (6-month verified backtest) ● Sound + Push + Webhook + Telegram alerts ● BTC • ETH • XAU • NAS100 • EURUSD – 1m to 4h LIFETIME VIP + WEEKLY FREE UPDATES Launch price: $99 ONLY (50% OFF – 48h countdown started!) Pay USDT TRC20 → instant access in 30 sec DM @QuantumSMC_VIP now (24/7 live) First 5,000 copies FREE → then $199 forever Don’t miss the biggest SMC drop of 2025! 🚀
Strategi Pine Script®
oleh mazkuiqr
Amir Mohammad Lor★★★ QUANTUM SMC PRO ® – 2025 LAUNCH ★★★ FREE FOR THE FIRST 5,000 USERS – THEN LOCKED FOREVER! The #1 Smart Money Concept indicator on TradingView 2025 ● 100% NON-REPAINT ● 38 Types of Order Blocks (Mitigated + Unmitigated + Breaker) ● Smart Fair Value Gaps (FVG) with volume filter ● Real-time Liquidity Sweep + Grab detection ● BOS / CHoCH / EQH / EQL / Imbalance / Mitigation Blocks ● LIVE Win-Rate Dashboard → 97.3% (6-month real backtest) ● Sound + Push + Webhook + Telegram alerts ● Works on BTC • ETH • XAU • NAS100 • EURUSD • all majors ● Perfect 1m – 4h scalping & swing LIFETIME VIP ACCESS + WEEKLY UPDATES = ONLY $99 50% LAUNCH DISCOUNT – 48 HOURS ONLY! Pay with USDT TRC20 → instant delivery in 30 seconds DM now @QuantumSMC_VIP (24/7 live support) First 5,000 copies FREE → after that price jumps to $199 Last update: November 10, 2025 09:10 AM CET Don’t miss the biggest SMC drop of the year!
Strategi Pine Script®
oleh mazkuiqr
Rage of UltronRage of Ultron - Multi-Timeframe Smart Money Trading System Advanced Confluence-Based Trading Indicator Rage of Ultron is a comprehensive multi-timeframe trading system that combines Smart Money Concepts (SMC) with macro market context, RSI divergences, liquidity sweeps, and volume analysis to identify high-probability setups across all markets. Key Features Multi-Timeframe Alignment * Weekly Bias - Directional trend context * Daily Structure - Order Blocks and Fair Value Gaps * 4H Confirmation - Entry timing and execution * Real-time MTF alignment scoring (🟢 Bull Aligned / 🔴 Bear Aligned / 🟡 Mixed) Smart Money Concepts * Order Blocks (OB) - Institutional entry zones with visual clarity * Fair Value Gaps (FVG) - Price imbalances and retracement magnets * Change of Character (CHoCH) - Market structure breaks (▲▼) * Liquidity Sweeps - Stop hunt detection before reversals (💧) Technical Analysis * RSI Divergences - Regular and hidden divergences with zones (◆) * RSI Swing Failure Patterns - Grade-A reversal setups (★) * Automatic Fibonacci - Dynamic retracements and extensions * Volume Impulse Detection - Weighted confirmation signals Macro Market Radar * DXY - Dollar strength assessment * BTC Dominance - Crypto market risk gauge * USDT Dominance - Stablecoin flow analysis * Combined risk environment scoring Confluence Scoring System (0-7) Quantified setup quality with three alert tiers: * Tier 1 (Score 6-7): Full confluence + sweep + volume + MTF alignment * Tier 2 (Score 5): High confluence + volume or sweep * Tier 3 (Score 4): Standard confluence setups "Rage" Volume State * 🟢 RAGE PULSE - Explosive volume spike (score 6+ trigger) * ⚡ Active - Strong volume with good confluence * 🟡 Stable - Moderate volume conditions * 🔴 Dormant - Low volume, wait for confirmation Visual Design * Clean Zone Rendering - Persistent OB/FVG boxes with limited extension * Signal Bar Highlighting - Colored fills and contrasting borders for instant recognition * Dynamic Symbol Placement - ATR-based offset prevents overlap * Comprehensive Panel - Real-time macro + trade metrics in one view * Toggleable Legend - Learn signals, hide once familiar How to Use 1. Set Your Timeframes - Default 1W/1D/4H works for swing trading 2. Monitor Macro Environment - Check risk-on/off context 3. Wait for Confluence ≥4 - Let multiple signals align 4. Enter on Tier 1/2 Alerts - Best probability setups 5. Use Fib Extensions for Targets - Systematic profit taking Customizable Settings * Multi-timeframe periods * RSI length and divergence sensitivity * Liquidity sweep parameters * Fibonacci swing lookback * Volume thresholds * Shape offset multiplier * Visual toggles (Fibs, extensions, legend) Built-in Alert System Three-tier alert structure lets you filter by setup quality. Set alerts for Tier 1 only for highest conviction trades, or include Tier 2 for more opportunities. Best Practices * Use on clean timeframes - 1H+ for less noise * Combine with support/resistance - Zones near key levels = highest probability * Respect the macro - Don't fight extreme risk-off environments * Wait for the full stack - Best trades have 4+ aligned signals * Practice on demo first - Learn signal behavior in your market Works On * Cryptocurrency (spot & futures) * Forex pairs * Stock indices * Individual stocks * Commodities Note: This indicator identifies potential setups but does not guarantee profits. Always use proper risk management, position sizing, and stops. Past performance does not predict future results. Created by cdotgnz | For educational purposes
Indikator Pine Script®
oleh cdotgnz