Schaff Trend Cycle (STC) - t0rdn3Schaff Trend Cycle (STC)
By t0rdn3 (original STC by , now with more descriptive naming)
Description
The Schaff Trend Cycle (STC) is a momentum-based oscillator that combines the speed of a fast EMA crossover with cyclical normalization. Developed by Doug Schaff, it identifies market turning points more responsively than MACD or RSI.
How It Works
1. EMA Difference : Calculates the difference between two EMAs of the source series (default: close).
2. Cycle Percentage : Normalizes that difference to a 0–100 range over the cycle period.
3. Smoothing : Applies exponential smoothing twice—first to the cycle percentage, then to its normalized cycles—to reduce noise.
4. Final STC Line : Produces a smoothed oscillator oscillating between 0 and 100.
Alerts
- "STC turned down above 75" : Fires once when STC makes a local peak above the upper threshold ( 75 ).
- "STC turned up below 25" : Fires once when STC makes a local trough below the lower threshold ( 25 ).
Inputs
Cycle Period : 12 — Lookback in bars for normalization
Fast EMA Length : 26 — Period of the fast EMA
Slow EMA Length : 50 — Period of the slow EMA
Smoothing Factor : 0.5 — Exponential smoothing coefficient (0–1)
Usage
Readings above 75 indicate an overbought cycle; readings below 25 indicate an oversold cycle. Crossings of the 50 midline can confirm trend direction:
- STC rising through 50 → bullish shift
- STC falling through 50 → bearish shift
Combine STC with price action or other trend filters to improve signal quality. You can adjust the cycle period and EMA lengths to match different timeframes or instruments.
In den Scripts nach "12月4号是什么星座" suchen
Rate of Change HistogramExplanation of Modifications
Converting ROC to Histogram:
Original ROC: The ROC is calculated as roc = 100 * (source - source ) / source , plotted as a line oscillating around zero.
Modification: Instead of plotting roc as a line, it’s now plotted as a histogram using style=plot.style_columns. This makes the ROC values visually resemble the MACD histogram, with bars extending above or below the zero line based on momentum.
Applying MACD’s Four-Color Scheme:
Logic: The histogram’s color is determined by:
Above Zero (roc >= 0): Bright green (#26A69A) if ROC is rising (roc > roc ), light green (#B2DFDB) if falling (roc < roc ).
Below Zero (roc < 0): Bright red (#FF5252) if ROC is falling (roc < roc ), light red (#FFCDD2) if rising (roc > roc ).
Implementation: Used the exact color logic and hex codes from the MACD code, applied to the ROC histogram. This highlights momentum ebbs (falling ROC, fading waves) and flows (rising ROC, strengthening waves).
Removing Signal Line:
Unlike the previous attempt, no signal line is added. The histogram is purely the ROC value, ensuring it directly reflects price change momentum without additional smoothing, making it faster and more responsive to pulse waves, as you indicated ROC performs better than other oscillators.
Alert Conditions:
Added alerts to match the MACD’s logic, triggering when the ROC histogram crosses the zero line:
Rising to Falling: When roc >= 0 and roc < 0, signaling a potential wave peak (e.g., end of Wave 3 or C).
Falling to Rising: When roc <= 0 and roc > 0, indicating a potential wave bottom (e.g., start of Wave 1 or rebound).
These alerts help identify transitions in 3-4 wave pulse patterns.
Plotting:
Histogram: Plotted as columns (plot.style_columns) with the four-color scheme, directly representing ROC momentum.
Zero Line: Kept the gray zero line (#787B86) for reference, consistent with the MACD.
Removed ROC Line/Signal Line: Since you want the ROC to act as the histogram itself, no additional lines are plotted.
Inputs:
Retained the original length (default 9) and source (default close) inputs for consistency.
Removed signal-related inputs (e.g., signal_length, sma_signal) as they’re not needed for a pure ROC histogram.
How This ROC Histogram Works for Wave Pulses
Wave Alignment:
Above Zero (Bullish Momentum): Positive ROC bars indicate flows (e.g., impulse Waves 1, 3, or rebounds in Wave B/C). Bright green bars show accelerating momentum (strong pulses), while light green bars suggest fading momentum (potential wave tops).
Below Zero (Bearish Momentum): Negative ROC bars indicate ebbs (e.g., corrective Waves 2, 4, A, or C). Bright red bars show increasing bearish momentum (strong pullbacks), while light red bars suggest slowing declines (potential wave bottoms).
3-4 Wave Pulses:
In a 3-wave A-B-C correction: Wave A (down) shows bright red bars (falling ROC), Wave B (up) shows bright/light green bars (rising ROC), and Wave C (down) shifts back to red bars.
In a 4-wave consolidation: Alternating green/red bars highlight the rhythmic ebbs and flows as momentum oscillates.
Timing:
Zero-line crossovers mark wave transitions (e.g., from Wave 2 to Wave 3).
Color changes (e.g., bright to light green) signal momentum shifts within waves, helping identify pulse peaks/troughs.
Advantages Over MACD:
The ROC histogram is more responsive than the MACD histogram because ROC directly measures price change percentage, while MACD relies on moving average differences, which introduce lag. This makes the ROC histogram better for capturing rapid 3-4 wave pulses, as you noted.
Example Usage
For a stock with 3-4 wave pulses on a 5-minute chart:
Wave 1 (Flow): ROC rises above zero, histogram turns bright green (rising momentum), indicating a strong bullish pulse.
Wave 2 (Ebb): ROC falls below zero, histogram shifts to bright red (falling momentum), signaling a corrective pullback.
Wave 3 (Flow): ROC crosses back above zero, histogram becomes bright green again, confirming a powerful pulse.
Wave 4 (Ebb): ROC dips slightly, histogram turns light green (falling momentum above zero) or light red (rising momentum below zero), indicating consolidation.
Alerts trigger on zero-line crosses (e.g., from Wave 2 to Wave 3), helping time trades.
Settings Recommendations
Default (length=9): Works well for most time frames, balancing sensitivity and smoothness.
Intraday Pulses: Use length=5 or length=7 for faster signals on 5-minute or 15-minute charts.
Daily Charts: Try length=12 or length=14 for broader wave cycles.
Testing: Apply to a stock with clear wave patterns (e.g., tech stocks like AAPL or TSLA) and adjust length to match the pulse frequency you observe.
Notes
Confirmation: Pair the ROC histogram with price action (e.g., Fibonacci retracements, support/resistance) to validate wave counts, as momentum oscillators can be noisy in choppy markets.
Divergences: Watch for divergences (e.g., price makes a higher high, but ROC histogram bars are lower) to spot wave reversals, especially at Wave 3 or C ends.
Comparison to MACD: The ROC histogram is faster and more direct, making it ideal for short-term pulse waves, but it may be more volatile, so use with technical levels for precision.
Zweig Breadth ThrustZweig Breadth Thrust Detector
This indicator tracks one of the rarest and most powerful bullish signals in market history: the Zweig Breadth Thrust.
It calculates the 10-day moving average of NYSE advancing stocks divided by the sum of advancing and declining stocks. When the breadth reading surges from deeply oversold (<0.40) to explosively bullish (>0.615) within just 10 trading days, it signals a momentum reset so intense that it often marks the start of major new bull runs.
Zweig Thrusts are extremely rare — but when they occur, historical odds favor significant market gains over the next 6 to 12 months.
This tool doesn't just chase price — it measures raw internal strength across the entire market.
When the masses panic, and the army of stocks surges together — that's when legends are made.
Williams R Zone Scalper v1.0[BullByte]Originality & Usefulness
Unlike standard Williams R cross-over scripts, this strategy layers five dynamic filters—moving-average trend, Supertrend, Choppiness Index, Bollinger Band Width, and volume validation —and presents a real-time dashboard with equity, PnL, filter status, and key indicator values. No other public Pine script combines these elements with toggleable filters and a custom dashboard. In backtests (BTC/USD (Binance), 5 min, 24 Mar 2025 → 28 Apr 2025), adding these filters turned a –2.09 % standalone Williams R into a +5.05 % net winner while cutting maximum drawdown in half.
---
What This Script Does
- Monitors Williams R (length 14) for overbought/oversold reversals.
- Applies up to five dynamic filters to confirm trend strength and volatility direction:
- Moving average (SMA/EMA/WMA/HMA)
- Supertrend line
- Choppiness Index (CI)
- Bollinger Band Width (BBW)
- Volume vs. its 50-period MA
- Plots blue arrows for Long entries (R crosses above –80 + all filters green) and red arrows for Short entries (R crosses below –20 + all filters green).
- Optionally sets dynamic ATR-based stop-loss (1.5×ATR) and take-profit (2×ATR).
- Shows a dashboard box with current position, equity, PnL, filter status, and real-time Williams R / MA/volume values.
---
Backtest Summary (BTC/USD(Binance), 5 min, 24 Mar 2025 → 28 Apr 2025)
• Total P&L : +50.70 USD (+5.05 %)
• Max Drawdown : 31.93 USD (3.11 %)
• Total Trades : 198
• Win Rate : 55.05 % (109/89)
• Profit Factor : 1.288
• Commission : 0.01 % per trade
• Slippage : 0 ticks
Even in choppy March–April, this multi-filter approach nets +5 % with a robust risk profile, compared to –2.09 % and higher drawdown for Williams R alone.
---
Williams R Alone vs. Multi-Filter Version
• Total P&L :
– Williams R alone → –20.83 USD (–2.09 %)
– Multi-Filter → +50.70 USD (+5.05 %)
• Max Drawdown :
– Williams R alone → 62.13 USD (6.00 %)
– Multi-Filter → 31.93 USD (3.11 %)
• Total Trades : 543 vs. 198
• Win Rate : 60.22 % vs. 55.05 %
• Profit Factor : 0.943 vs. 1.288
---
Inputs & What They Control
- wrLen (14): Williams R look-back
- maType (EMA): Trend filter type (SMA, EMA, WMA, HMA)
- maLen (20): Moving-average period
- useChop (true): Toggle Choppiness Index filter
- ciLen (12): CI look-back length
- chopThr (38.2): CI threshold (below = trending)
- useVol (true): Toggle volume-above-average filter
- volMaLen (50): Volume MA period
- useBBW (false): Toggle Bollinger Band Width filter
- bbwMaLen (50): BBW MA period
- useST (false): Toggle Supertrend filter
- stAtrLen (10): Supertrend ATR length
- stFactor (3.0): Supertrend multiplier
- useSL (false): Toggle ATR-based SL/TP
- atrLen (14): ATR period for SL/TP
- slMult (1.5): SL = slMult × ATR
- tpMult (2.0): TP = tpMult × ATR
---
How to Read the Chart
- Blue arrow (Long): Williams R crosses above –80 + all enabled filters green
- Red arrow (Short) : Williams R crosses below –20 + all filters green
- Dashboard box:
- Top : position and equity
- Next : cumulative PnL in USD & %
- Middle : green/white dots for each filter (green=passing, white=disabled)
- Bottom : Williams R, MA, and volume current values
---
Usage Tips
- Add the script : Indicators → My Scripts → Williams R Zone Scalper v1.0 → Add to BTC/USD chart on 5 min.
- Defaults : Optimized for BTC/USD.
- Forex majors : Raise `chopThr` to ~42.
- Stocks/high-beta : Enable `useBBW`.
- Enable SL/TP : Toggle `useSL`; stop-loss = 1.5×ATR, take-profit = 2×ATR apply automatically.
---
Common Questions
- * Why not trade every Williams R reversal?*
Raw Williams R whipsaws in sideways markets. Choppiness and volume filters reduce false entries.
- *Can I use on 1 min or 15 min?*
Yes—adjust ATR length or thresholds accordingly. Defaults target 5 min scalping.
- *What if all filters are on?*
Fewer arrows, higher-quality signals. Expect ~10 % boost in average win size.
---
Disclaimer & License
Trading carries risk of loss. Use this script “as is” under the Mozilla Public License 2.0 (mozilla.org). Always backtest, paper-trade, and adjust risk settings to your own profile.
---
Credits & References
- Pine Script v6, using TradingView’s built-in `ta.supertrend()`.
- TradingView House Rules: www.tradingview.com
Goodluck!
BullByte
Smart Market Matrix Smart Market Matrix
This indicator is designed for intraday, scalping, providing automated detection of price pivots, liquidity traps, and breakout confirmations, along with a context dashboard featuring volatility, trend, and volume.
## Summary Description
### Menu Settings & Their Roles
- **Swing Pivot Strength**: Controls the sensitivity for detecting High/Low pivots.
- **Show Pivot Points**: Toggles the display of HH/LL markers on the chart.
- **VWMA Length for Trap Volume** & **Volume Spike Multiplier**: Identify concentrated volume spikes for liquidity traps.
- **Wick Ratio Threshold** & **Max Body Size Ratio**: Detect candles with disproportionate wicks and small bodies (doji-ish) for traps.
- **ATR Length for Trap**: Measures volatility specific to trap detection.
- **VWMA Length for Breakout Volume**, **ATR Multiplier for Breakout**, **ATR Length for Breakout**, **Min Body/Range Ratio**: Set adaptive breakout thresholds based on volatility and volume.
- **OBV Smooth Length**: Smooths OBV momentum for breakout confirmation.
- **Enable VWAP Filter for Confirmations**: Optionally validate breakouts against the VWAP.
- **Enable Higher-TF Trend Filter** & **Trend Filter Timeframe**: Align breakout signals with the 1h/4h/Daily trend.
- **ADX Length**, **EMA Fast/Slow Length for Context**: Parameters for the context dashboard (Volatility, Trend, Volume).
- **Show Intraday VWAP Line**, **VWAP Line Color/Width**: Display the intraday VWAP line with custom style.
### Signal Interpretation Map
| Signal | Description | Recommended Action |
|--------------------------------|-----------------------------------------------------------|-------------------------------------------|
| 📌 **HH / LL (pivot)** | Market structure (support/resistance) | Note key levels |
| **Bull Trap(green diamond)** | Sweep down + volume spike + wick + rejection | Go long with trend filter
| **Bear Trap(red diamond)** | Sweep up + volume spike + wick + rejection | Go short with trend filter
| 🔵⬆️ **Breakout Confirmed Up** | Close > ATR‑scaled high + volume + OBV↑ | Go long with trend filter |
| 🔵⬇️ **Breakout Confirmed Down** | Close < ATR‑scaled low + volume + OBV↓ | Go short with trend filter |
| 📊 **VWAP Line** | Intraday reference to guide price | Use as dynamic support/resistance |
| ⚡ **Volatility** | ATR ratio High/Med/Low | Adjust position size |
| 📈 **Trend Context** | ADX+EMA Strong/Moderate/Weak | Confirm trend direction |
| 🔍 **Volume Context** | Breakout / Rising / Falling / Calm | Check volume momentum |
*This summary gives you a quick overview of the key settings and how to interpret signals for efficient intraday scalping.*
### Suggested Settings
- **Intraday Scalping (5m–15m)**
- `Swing Pivot Strength = 5`
- `VWMA Length for Trap Volume = 10`, `Volume Spike Multiplier = 1.6`
- `ATR Length for Trap = 7`
- `VWMA Length for Breakout Volume = 12`, `ATR Length for Breakout = 9`, `ATR Multiplier for Breakout = 0.5`
- `Min Body/Range Ratio for Breakout = 0.5`, `OBV Smooth Length = 7`
- `Enable Higher-TF Trend Filter = true` (TF = 60)
- `Show Intraday VWAP Line = true` (Color = orange, Width = 2)
- **Swing Trading (4h–Daily)**
- `Swing Pivot Strength = 10`
- `VWMA Length for Trap Volume = 20`, `Volume Spike Multiplier = 2.0`
- `ATR Length for Trap = 14`
- `VWMA Length for Breakout Volume = 30`, `ATR Length for Breakout = 14`, `ATR Multiplier for Breakout = 0.8`
- `Min Body/Range Ratio for Breakout = 0.7`, `OBV Smooth Length = 14`
- `Enable Higher-TF Trend Filter = true` (TF = D)
- `Show Intraday VWAP Line = false`
*Adjust these values based on the symbol and market volatility for optimal performance.*
Gold ORB Strategy (15-min Range, 5-min Entry)The Gold ORB (Opening Range Breakout) Strategy is designed for day traders looking to capitalize on the price action in the early part of the trading day, specifically using a 15-minute range for identifying the opening range and a 5-minute timeframe for breakout entries. The strategy trades the Gold market (XAU/USD) during the New York session.
Opening Range: The strategy defines the Opening Range (ORB) between 9:30 AM EST and 9:45 AM EST using the highest and lowest points during this 15-minute window.
Breakout Entries: The strategy enters trades when the price breaks above the ORB high for a long position or below the ORB low for a short position. It waits for a 5-minute candle close outside the range before entering a trade.
Stop Loss and Take Profit: The stop loss is placed at 50% of the ORB range, and the take profit is set at twice the ORB range (1:2 risk-reward ratio).
Time Window: The strategy only executes trades before 12:00 PM EST, avoiding late-day market fluctuations and consolidations.
Clenow MomentumClenow Momentum Method
The Clenow Momentum Method, developed by Andreas Clenow, is a systematic, quantitative trading strategy focused on capturing medium- to long-term price trends in financial markets. Popularized through Clenow’s book, Stocks on the Move: Beating the Market with Hedge Fund Momentum Strategies, the method leverages momentum—an empirically observed phenomenon where assets that have performed well in the recent past tend to continue performing well in the near future.
Theoretical Foundation
Momentum investing is grounded in behavioral finance and market inefficiencies. Investors often exhibit herding behavior, underreact to new information, or chase trends, causing prices to trend beyond fundamental values. Clenow’s method builds on academic research, such as Jegadeesh and Titman (1993), which demonstrated that stocks with high returns over 3–12 months outperform those with low returns over similar periods.
Clenow’s approach specifically uses **annualized momentum**, calculated as the rate of return over a lookback period (typically 90 days), annualized to reflect a yearly percentage. The formula is:
Momentum=(((Close N periods agoCurrent Close)^N252)−1)×100
- Current Close: The most recent closing price.
- Close N periods ago: The closing price N periods back (e.g., 90 days).
- N: Lookback period (commonly 90 days).
- 252: Approximate trading days in a year for annualization.
This metric ranks stocks by their momentum, prioritizing those with the strongest upward trends. Clenow’s method also incorporates risk management, diversification, and volatility adjustments to enhance robustness.
Methodology
The Clenow Momentum Method involves the following steps:
1. Universe Selection:
- A broad universe of liquid stocks is chosen, often from major indices (e.g., S&P 500, Nasdaq 100) or global exchanges.
- Filters should exclude illiquid stocks (e.g., low average daily volume) or those with extreme volatility.
2. Momentum Calculation:
- Stocks are ranked based on their annualized momentum over a lookback period (typically 90 days, though 60–120 days can be common tests).
- The top-ranked stocks (e.g., top 10–20%) are selected for the portfolio.
3. Volatility Adjustment (Optional):
- Clenow sometimes adjusts momentum scores by volatility (e.g., dividing by the standard deviation of returns) to favor stocks with smoother trends.
- This reduces exposure to erratic price movements.
4. Portfolio Construction:
- A diversified portfolio of 10–25 stocks is constructed, with equal or volatility-weighted allocations.
- Position sizes are often adjusted based on risk (e.g., 1% of capital per position).
5. Rebalancing:
- The portfolio is rebalanced periodically (e.g., weekly or monthly) to maintain exposure to high-momentum stocks.
- Stocks falling below a momentum threshold are replaced with higher-ranked candidates.
6. Risk Management:
- Stop-losses or trailing stops may be applied to limit downside risk.
- Diversification across sectors reduces concentration risk.
Implementation in TradingView
Key features include:
- Customizable Lookback: Users can adjust the lookback period in pinescript (e.g., 90 days) to align with Clenow’s methodology.
- Visual Cues: Background colors (green for positive, red for negative momentum) and a zero line help identify trend strength.
- Integration with Screeners: TradingView’s stock screener can filter high-momentum stocks, which can then be analyzed with the custom indicator.
Strengths
1. Simplicity: The method is straightforward, relying on a single metric (momentum) that’s easy to calculate and interpret.
2. Empirical Support: Backed by decades of academic research and real-world hedge fund performance.
3. Adaptability: Applicable to stocks, ETFs, or other asset classes, with flexible lookback periods.
4. Risk Management: Diversification and periodic rebalancing reduce idiosyncratic risk.
5. TradingView Integration: Pine Script implementation enables real-time visualization, enhancing decision-making for stocks like NVDA or SPY.
Limitations
1. Mean Reversion Risk: Momentum can reverse sharply in bear markets or during sector rotations, leading to drawdowns.
2. Transaction Costs: Frequent rebalancing increases trading costs, especially for retail traders with high commissions. This is not as prevalent with commission free trading becoming more available.
3. Overfitting Risk: Over-optimizing lookback periods or filters can reduce out-of-sample performance.
4. Market Conditions: Underperforms in low-momentum or highly volatile markets.
Practical Applications
The Clenow Momentum Method is ideal for:
Retail Traders: Use TradingView’s screener to identify high-momentum stocks, then apply the Pine Script indicator to confirm trends.
Portfolio Managers: Build diversified momentum portfolios, rebalancing monthly to capture trends.
Swing Traders: Combine with volume filters to target short-term breakouts in high-momentum stocks.
Cross-Platform Workflow: Integrate with Python scanners to rank stocks, then visualize on TradingView for trade execution.
Comparison to Other Strategies
Vs. Minervini’s VCP: Clenow’s method is purely quantitative, while Minervini’s Volatility Contraction Pattern (your April 11, 2025 query) combines momentum with chart patterns. Clenow is more systematic but less discretionary.
Vs. Mean Reversion: Momentum bets on trend continuation, unlike mean reversion strategies that target oversold conditions.
Vs. Value Investing: Momentum outperforms in bull markets but may lag value strategies in recovery phases.
Conclusion
The Clenow Momentum Method is a robust, evidence-based strategy that capitalizes on price trends while managing risk through diversification and rebalancing. Its simplicity and adaptability make it accessible to retail traders, especially when implemented on platforms like TradingView with custom Pine Script indicators. Traders must be mindful of transaction costs, mean reversion risks, and market conditions. By combining Clenow’s momentum with volume filters and alerts, you can optimize its application for swing or position trading.
Bullish and Bearish Breakout Alert for Gold Futures PullbackBelow is a Pine Script (version 6) for TradingView that includes both bullish and bearish breakout conditions for my intraday trading strategy on micro gold futures (MGC). The strategy focuses on scalping two-legged pullbacks to the 20 EMA or key levels with breakout confirmation, tailored for the Apex Trader Funding $300K challenge. The script accounts for the Daily Sentiment Index (DSI) at 87 (overbought, favoring pullbacks). It generates alerts for placing stop-limit orders for 175 MGC contracts, ensuring compliance with Apex’s rules ($7,500 trailing threshold, $20,000 profit target, 4:59 PM ET close).
Script Requirements
Version: Pine Script v6 (latest for TradingView, April 2025).
Purpose:
Bullish: Alert when price breaks above a rejection candle’s high after a two-legged pullback to the 20 EMA in a bullish trend (price above 20 EMA, VWAP, higher highs/lows).
Bearish: Alert when price breaks below a rejection candle’s low after a two-legged pullback to the 20 EMA in a bearish trend (price below 20 EMA, VWAP, lower highs/lows).
Context: 5-minute MGC chart, U.S. session (8:30 AM–12:00 PM ET), avoiding overbought breakouts above $3,450 (DSI 87).
Output: Alerts for stop-limit orders (e.g., “Buy: Stop=$3,377, Limit=$3,377.10” or “Sell: Stop=$3,447, Limit=$3,446.90”), quantity 175 MGC.
Apex Compliance: 175-contract limit, stop-losses, one-directional news trading, close by 4:59 PM ET.
How to Use the Script in TradingView
1. Add Script:
Open TradingView (tradingview.com).
Go to “Pine Editor” (bottom panel).
Copy the script from the content.
Click “Add to Chart” to apply to your MGC 5-minute chart .
2. Configure Chart:
Symbol: MGC (Micro Gold Futures, CME, via Tradovate/Apex data feed).
Timeframe: 5-minute (entries), 15-minute (trend confirmation, manually check).
Indicators: Script plots 20 EMA and VWAP; add RSI (14) and volume manually if needed .
3. Set Alerts:
Click the “Alert” icon (bell).
Add two alerts:
Bullish Breakout: Condition = “Bullish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Bearish Breakout: Condition = “Bearish Breakout Alert for Gold Futures Pullback,” trigger = “Once Per Bar Close.”
Customize messages (default provided) and set notifications (e.g., TradingView app, SMS).
Example: Bullish alert at $3,377 prompts “Stop=$3,377, Limit=$3,377.10, Quantity=175 MGC” .
4. Execute Orders:
Bullish:
Alert triggers (e.g., stop $3,377, limit $3,377.10).
In TradingView’s “Order Panel,” select “Stop-Limit,” set:
Stop Price: $3,377.
Limit Price: $3,377.10.
Quantity: 175 MGC.
Direction: Buy.
Confirm via Tradovate.
Add bracket order (OCO):
Stop-loss: Sell 175 at $3,376.20 (8 ticks, $1,400 risk).
Take-profit: Sell 87 at $3,378 (1:1), 88 at $3,379 (2:1) .
Bearish:
Alert triggers (e.g., stop $3,447, limit $3,446.90).
Select “Stop-Limit,” set:
Stop Price: $3,447.
Limit Price: $3,446.90.
Quantity: 175 MGC.
Direction: Sell.
Confirm via Tradovate.
Add bracket order:
Stop-loss: Buy 175 at $3,447.80 (8 ticks, $1,400 risk).
Take-profit: Buy 87 at $3,446 (1:1), 88 at $3,445 (2:1) .
5. Monitor:
Green triangles (bullish) or red triangles (bearish) confirm signals.
Avoid bullish entries above $3,450 (DSI 87, overbought) or bearish entries below $3,296 (support) .
Close trades by 4:59 PM ET (set 4:50 PM alert) .
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation
This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds.
1. Introduction
Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels.
2. Theoretical Foundation
2.1 Momentum Component
The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems.
2.2 Keltner Channels
Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978).
2.3 Stochastic Oscillator Principles
The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value.
3. Strategy Methodology
3.1 Entry and Exit Conditions
The strategy employs a contrarian approach within the channel framework:
Long Entry Condition:
Close price > Close price periods ago (momentum filter)
KeltnerStochastic < threshold (oversold within channel)
Short Entry Condition:
Close price < Close price periods ago (momentum filter)
KeltnerStochastic > threshold (overbought within channel)
Exit Conditions:
Exit long positions when KeltnerStochastic > threshold
Exit short positions when KeltnerStochastic < threshold
This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters.
3.2 Risk Management
Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated.
3.3 Dynamic Position Sizing
The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance:
$ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$
This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity.
4. Empirical Performance Analysis
Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics:
Enhanced performance during trending markets with moderate volatility
Reduced drawdowns during choppy market conditions through the dual-filter approach
Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008)
5. Strategy Limitations and Future Research
While effective in many market conditions, this strategy faces challenges during:
Rapid volatility expansion events where stop-loss mechanisms may be inadequate
Prolonged sideways markets with insufficient momentum
Markets with structural changes in volatility profiles
Future research should explore:
Adaptive threshold parameters based on regime detection
Integration with additional confirmatory indicators
Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016)
References
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91.
Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons.
Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926.
Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service.
Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250.
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons.
Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group.
Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300.
Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
MACD Bullish Cross Alert📘 Indicator Description – MACD Bullish Cross Alert
This indicator is designed to detect bullish momentum shifts using the classic MACD (Moving Average Convergence Divergence) crossover strategy.
Key Features:
Calculates the MACD Line and Signal Line using customizable inputs (default: 12, 26, 9).
Triggers an alert when the MACD Line (blue) crosses above the Signal Line (orange).
Helps identify early bullish trend reversals or momentum entry points.
Ideal for swing traders, position traders, and crypto investors using the weekly timeframe.
How to Use:
Add to any chart and set the timeframe to 1W (weekly).
Create an alert using the built-in MACD Bullish Crossover condition.
Combine with price action, volume, or RSI for higher conviction entries.
Use Cases:
Spotting early entry points after long downtrends.
Confirming a trend reversal in high timeframes.
Generating high-probability entries in trend-following systems.
MACD [AlchimistOfCrypto]🌠 MACD Optimized with Python – Decoding the Chaos of Markets 🌠
Category: Trend Analysis 📈
"Like the dynamic systems studied in chaos theory, financial markets appear unpredictable at first glance. Yet, as Edward Lorenz demonstrated, even in apparent chaos reside harmonious mathematical structures. The MACD (Moving Average Convergence Divergence) represents this quest for order within disorder—a mathematical formulation that extracts coherent signals from price noise. By combining moving averages of different periods, this indicator reveals hidden cycles and precise moments when market energy shifts, like a pendulum obeying the immutable laws of physics."
📊 Technical Overview
The MACD Optimized with Python is a revolutionary take on the classic Moving Average Convergence Divergence indicator. Powered by Python-driven optimizations 🐍, it adapts to specific timeframes, delivering razor-sharp signals for traders seeking to navigate the market’s chaos with precision.
⚙️ How It Works
- Python-Optimized Parameters 🔧: Unlike the standard MACD (12,26,9), our version uses mathematically tailored parameters for each timeframe:
- 1H: 11/38/27
- 4H: 9/98/27
- 1D: 45/90/29
- 1W: 9/16/3
- 2W: 5/20/5
- Intuitive Visuals 🎨:
- Crossovers marked by colored dots 🟢🔴 for clear entry/exit signals.
- Histogram with a color gradient 🌈 to show direction and momentum intensity.
- Customizable Signals 🎯: Choose to display long, short, or both signals to match your trading style.
🚀 How to Use This Indicator
1. Select Your Timeframe ⏰: Choose the timeframe aligned with your trading horizon (1H, 4H, 1D, 1W, or 2W).
2. Spot Crossovers 🔍: Watch for the MACD line (green) crossing the signal line (red) to identify potential trend changes.
3. Confirm with Divergence ✅: Combine crossovers with price-MACD divergence for high-probability trend reversal signals.
📅 Release Notes
Unlock the hidden order of markets with this Python-optimized MACD. Stay tuned for future enhancements! ✨
🏷️ Tags
#Trading #TechnicalAnalysis #MACD #TrendAnalysis #Python #MultiTimeframe #Divergence #Momentum #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #ChaosTheory #OptimizedTrading
Change of Character FanChange of Character Fan
Overview
The Change of Character Fan is designed to help traders detect shifts (changes of character) in market direction and sentiment before they become fully visible through traditional candlestick analysis. Instead of relying solely on the shape or close of candlesticks, this indicator offers a direct, real-time look at the internal price action occurring within a single bar. This visibility into intrabar dynamics can potentially allow traders to enter or exit trades earlier, minimize false signals, and reduce their dependence on multiple lower-timeframe charts.
How it Works:
The indicator plots a "fan" consisting of five distinct slope lines within the current bar. Each line represents the internal trend of price movement based on user-defined lower timeframe data intervals.
By default, these intervals are set to 3, 5, 8, 13, and 21 samples from 1-second timeframe data.
Each line only appears when it has collected the minimum required number of intrabar data points.
The fan lines use a progressive opacity scale (lighter to darker), visually highlighting the confidence level or probability of directional continuation within the current bar.
At the open of every new bar, the fan disappears completely and gradually reappears as new data is gathered, ensuring clarity and eliminating outdated signals.
Understanding the Mathematics: Linear Regression Model
This indicator is built around the concept of a linear regression model. Linear regression is a statistical technique used to model and analyze relationships between variables—in this case, time (independent variable) and price (dependent variable).
How Linear Regression Works:
Linear regression fits a straight line (called a "line of best fit") through a set of data points, minimizing the overall distance between each point and the line itself.
Mathematically, this is achieved by minimizing the squared differences (errors) between the observed values (actual prices) and the predicted values (prices on the line).
The linear model used here can be expressed in the form:
y = mx + b
where:
𝑦
y is the predicted price,
𝑥
x represents time (each data sample interval),
𝑚
m is the slope of the line, representing the direction and velocity of the trend,
𝑏
b is the intercept (the theoretical price when x=0).
Why a Linear Model is Beneficial in this Indicator:
Simplicity and Reliability: Linear regression is simple, robust, and widely accepted as a baseline predictive model. It requires minimal computational resources, providing instant updates in real-time trading conditions.
Immediate Directional Feedback: The slope derived from linear regression immediately communicates the directional tendency of recent price action. A positive slope indicates upward pressure, and a negative slope signals downward pressure.
Noise Reduction: Even when price fluctuations are noisy or erratic, linear regression summarizes overall direction clearly, making it easier to detect genuine directional shifts (change of character) rather than random price noise.
Intrabar Analysis: Traditional candlestick analysis relies on fully formed candles, potentially delaying signals. By using linear regression on very short-term (intrabar) data, traders can detect shifts in momentum more quickly, providing an earlier signal than conventional candle patterns alone.
Practical Application:
This indicator helps traders to visually identify:
Early Trend Reversals: Intrabar analysis reveals momentum shifts potentially signaling reversals before they become obvious on conventional candles.
Momentum Continuations: Confidence is gained when all lines in the fan are clearly pointing in the same direction, indicating strong intrabar conviction.
Reduced False Signals: Traditional candlestick signals (e.g., hammer candles) sometimes produce false signals due to intrabar noise. By looking directly into intrabar dynamics, traders gain better context on whether candle patterns reflect genuine directional change or merely noise.
Important Requirements and Recommendations:
Subscription Requirements:
A TradingView subscription that supports sub-minute data (e.g., 1-second or 5-second resolution) is strongly recommended.
If your subscription doesn't include this data granularity, you must use a 1-minute lower timeframe, significantly reducing responsiveness. In this scenario, it's best suited for a 15-minute or higher chart, adjusting intervals to shorter periods.
Live Data Essential:
Real-time market data subscription is essential for the accuracy and effectiveness of this indicator.
Using delayed data reduces responsiveness and weakens the indicator's primary advantage.
Recommended Settings for Different Chart Timeframes:
1-minute chart: Use 1-second lower timeframe intervals (default intervals: 3, 5, 8, 13, 21).
5-minute chart: Adjust to a 5- or 10-second lower timeframe, possibly reducing intervals to shorter periods (e.g., 3, 5, 8, 10, 12).
15-minute or higher charts: Adjust lower timeframe to 1-minute if granular data is unavailable, with reduced interval lengths to maintain responsiveness.
Conclusion:
The Change of Character Fan empowers traders with early insight into directional shifts within each candle, significantly enhancing reaction speed, signal accuracy, and reducing dependency on multiple charts. Built on robust linear regression mathematics, it combines clarity, responsiveness, and ease-of-use in a powerful intrabar analysis tool.
Trade smarter, see sooner, and react faster.
Multi Timeframe Altered Money Flow Index by CoffeeShopCryptoMoney Flow Index is a long used tool in trading markets, understanding to where money is moving and most importantly when its going there.
One of the biggest challenges was the when part. Because seeing it on your current trading chart timeframe is easy but it gets difficult if youre attempting a top-down-analysis of market structure vs price performance.
The new formula presented by @CoffeeshopCrypto is a key solution to this timeframe analysis issue. Seems like I may have solved the "glitch-In-The-Matrix".
The issue was always setting a secondary MFI on your chart and telling the system you wanted to watch the 1 hour MFI from a 5 minute chart.
To do this you need to wait for 12 candles to close on your 5 minute chart before you can get a 1hour MFI value. The move may have already happend and you may be too late. If there was only a better faster way to see the changing values of the High Timeframe Money Flow Index in real time without changing chart times and losing place......oh wait.....there is one now!
This tool allows you to tell it what timeframe you are looking at,
and what you want to compare it to.
It runs the calculation in the background automatically to give you the real time values of your High Timeframe chart setting on the chart you are looking at.
How to trade Long
When both the LFT and HTF Money flow cross above ZERO, they are both in uptrend
How to trade Short
When both the LFT and HTF Money flow cross below ZERO, they are both in downtrend
What happens when Low timeframe is inside the high timeframe:
If High timeframe MFI is below zero but the LFT MFI is above it and still below zero, you have lost your short term downtrend. The opposite is true when the high timeframe MFI is above zero.
A strong constant comparative trend is when your low timeframe MFI is leading your High timeframe MFI.
Personal Settings:
In my usage, i find it best to multiply my trading chart timeframe by 3 and use that number as my high timeframe MFI setting
This works on ANY chart time you want. For example you are not locked to the standard built TradingView chart times.
If you trade on a 7 minute timeframe, you can set your HTF to 21.
7 * 3 = 21
Collatz Conjecture - DolphinTradeBot1️⃣ Overview
Every positive number follows its own unique path to reach 1 according to the Collatz rule.
Some numbers reach the end quickly and directly.
Others rise significantly before crashing down sharply.
Some get stuck within a certain range for a while before finally reaching 1.
Each number follows a different pattern — the number of steps it takes, how high it climbs, or which values it passes through cannot be predicted in advance.
This is a structure that appears chaotic but ultimately leads to order:
Every number reaches 1, but the way it gets there is entirely uncertain.
2️⃣ How Is It Work?
The rule is simple:
▪️ If the number is even → divide it by two.
▪️ If it’s odd → multiply it by three and add one.
Repeat this process at each step.
Example :
Let’s say the starting number is 7:
7 → 22 → 11 → 34 → 17 → 52 → 26 → 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1
It reaches 1 in 17 steps.
And from there, it always enters the same cycle:
4 → 2 → 1 → 4 → 2 → 1...
3️⃣ Why Is It Worth Learning?
🎯 This indicator isn’t just mathematical fun—it’s a thought experiment for those who dare to question market behavior.
▪️ It’s fun.
Watching numbers behave in unpredictable ways from a simple rule set is surprisingly enjoyable.
▪️ It shows how hard it is to teach a computer what randomness really is .
The Collatz process can be used to simulate chaotic behavior and may even inspire creative ways to introduce complexity into your code.
▪️ It makes you think — especially in financial markets.
The patternless, yet rule-based structure of Collatz can help train your mind to recognize that not all unpredictability is random. It’s a great mental model for navigating complex systems like price action.
▪️ Just like price movements in financial markets, this ancient problem remains unsolved.
Despite its simplicity, the Collatz conjecture has resisted proof for decades — a reminder that even the most basic-looking systems can hide deep complexity.
4️⃣ How To Use?
Super easy — in the indicator’s settings, there’s just one input field.
Enter any positive number, and you’ll see the pattern it follows on its way to 1.
You can also observe how many steps it takes and which values it visits in the info box at the top center of the chart.
5️⃣ Some Examples
You Can Observe the Chaos in the Following Examples⤵️
For Input Number → 12
For Input Number → 13
For Input Number → 14
For Input Number → 32768
For Input Number → 47
VWAP 2.0 with desv + Initial Balance by RiotWolftrading🌟 Overview
This powerful tool is designed for traders who want to harness the power of the Volume Weighted Average Price (VWAP) alongside session-based ranges to make informed trading decisions. Whether you're a day trader or a swing trader, this indicator provides a clean and effective way to identify support, resistance, and market trends—all in one place! 💡
✨ Key Features
Auto-Anchored VWAP 📊
Automatically calculates the VWAP based on a user-defined anchor period (e.g., Daily, Weekly, Monthly).
Resets at the start of each period (e.g., daily for a Daily anchor).
Displays a customizable VWAP line with standard deviation bands to highlight key price levels.
Standard Deviation Bands 📏
Plots up to three sets of standard deviation bands above and below the VWAP (multipliers: 1.0, 2.0, 3.0).
Includes volume percentage labels to show where trading volume is concentrated. 📉
Session High/Low Range 🕒
Identifies the high and low prices within a customizable session (default: 12:00 to 15:31).
Draws horizontal lines at the session high and low, with dotted deviation lines for additional reference points.
Perfect for spotting key levels during your trading session! 🔑
Time-Based Range Box ⏰
Highlights a specific time window (default: 15:40 to 15:50) with a colored box showing the high and low prices.
Ideal for tracking price action during high-impact events like news releases or market opens. 📅
Alerts 🚨
Set up alerts for when the price crosses above or below the VWAP—never miss a potential trading opportunity!
⚙️ Settings
Customize the indicator to fit your trading style with these easy-to-use settings:
VWAP Settings
Timezone 🌍: Select your timezone (default: GMT+2) to align calculations with your local time.
VWAP Source 📈: Choose the price source for VWAP (default: hlc3 - average of high, low, close).
Std Deviation Multipliers 📐: Adjust the multipliers for the bands (default: 1.0, 2.0, 3.0).
Line Width ✏️: Set the thickness of the VWAP and band lines (default: 1).
Session Time ⏳: Define the session window for VWAP calculations (default: 08:00-18:00, all days).
Show Upper/Lower Bands 👀: Toggle visibility for each set of bands (default: Band 1 visible, Bands 2 & 3 hidden).
Range Settings
Range Start/End Time 🕙: Set the time window for the range box (default: 15:40 to 15:50).
Box Color 🎨: Customize the border color (default: blue).
Box Background Color 🖌️: Adjust the background color (default: light aqua, 90% transparency).
I created this indicator to provide a streamlined, clutter-free tool for traders who rely on VWAP and session-based analysis. It focuses on the essentials—VWAP, standard deviation bands, session high/low, and range box—without unnecessary overlays. I hope it helps you in your trading journey! If you have feedback or suggestions, feel free to share—I’d love to hear from you! 😊
GranDoc - Week, Day, Month, and Session Separator5Indicator Name: GranDoc's - Week, Day, Month, and Session Separator
Version: Pine Script v5
Author: Jonpaul Nnamdi Opara (GranDoc )
Description
The "GranDoc - Week, Day, Month, and Session Separator" is a highly customizable TradingView indicator designed to enhance chart analysis by visually marking critical time-based transitions. Developed by Jonpaul Nnamdi Opara, this tool plots vertical lines with labels or background highlights to denote the start and end of weeks, days, months, and major trading sessions (Frankfurt, London, NY Morning, NY Afternoon, Sydney, and Tokyo). Traders can tailor colors, line styles, widths, transparency, and session times to align with their strategies and timezones.
Ideal for forex, stocks, futures, and crypto traders, this indicator simplifies the identification of key market periods—such as session openings/closings or new weeks—that often signal increased volatility or trend shifts. It’s optimized for intraday timeframes for session separators but supports all timeframes for week, day, and month markers, making it a versatile addition to any trader’s toolkit.
Features
Week Separators: Marks Monday starts with customizable lines and "Week Start" labels.
Day Separators: Highlights daily openings with lines and "Day Start" labels.
Month Separators: Indicates new months with lines and "Month Start" labels.
Session Separators: Plots lines and labels for major trading sessions’ start and end:
Frankfurt (default: 07:00–15:00 UTC)
London (default: 08:00–16:00 UTC)
NY Morning (default: 13:00–16:00 UTC)
NY Afternoon (default: 16:00–21:00 UTC)
Sydney (default: 22:00–06:00 UTC)
Tokyo (default: 00:00–08:00 UTC)
Timezone Support: Adjusts session times with a UTC offset (±12 hours).
Display Flexibility : Toggle between labeled vertical lines or background highlights.
Customization: Fine-tune colors, line styles (solid, dashed, dotted), widths, and transparency.
Background Mode: Highlights periods with translucent backgrounds for cleaner charts.
[ i]Labeled Lines: Each line includes descriptive labels (e.g., "London Open", "Tokyo Closed") when not in background mode.
How to Use
Add to Chart:
Copy the script into TradingView’s Pine Editor.
Click "Add to Chart" to apply the indicator.
Customize Settings:
Open settings via double-click or the "Settings" gear icon.
Timezone Offset: Set your UTC offset (e.g., -5 for EST) to align sessions.
Toggles: Enable/disable week, day, month, or session separators.
Appearance: Adjust colors, line styles, widths, and transparency for each separator.
Session Times: Modify start/end hours and minutes if defaults don’t suit your market.
Background Mode: Enable "Show as Background" for colored backgrounds instead of lines, and tweak "Session Background Transparency."
Labels: Labeled lines (e.g., "Sydney Open") appear automatically unless background mode is active.
Chart Compatibility:
Session separators require intraday timeframes (e.g., 1-minute to 4-hour).
Week, day, and month separators work across all timeframes.
Confirm your chart’s timezone aligns with your analysis.
Analyze:
Use separators to pinpoint session transitions, daily openings, or weekly shifts for trade planning.
Labels make it easy to spot key periods on busy charts.
Pair with indicators like RSI, volume, or support/resistance for deeper insights.
Example Use Cases
Forex Trading: Highlight London and NY session opens/closes for high-liquidity entries.
Day Trading: Reset strategies at daily separators and monitor intraday volatility.
Swing Trading: Use week/month separators to track longer-term trends.
Session Focus: Isolate sessions like Tokyo for regional market analysis.
Chart Clarity: Background mode declutters charts while marking key times.
Notes
Session separators are disabled on daily+ timeframes to prevent clutter.
Verify timezone offset for accurate session alignment.
Background mode suits lower timeframes for readability.
Labels are visible only when background mode is disabled.
Feedback
Share your thoughts or suggestions to make this indicator even better! Reach out via TradingView or connect with the author for insights. Happy trading!
About the Author
Dr. Jonpaul Nnamdi Opara, a PhD graduate from Ehime University, Japan, is a researcher and developer specializing in AI and machine learning. His work on automated landslide mapping and defect detection, published in journals like GEOMATE, showcases his precision-driven approach. With the "GranDoc" indicator, Jonpaul brings intuitive, data-driven clarity to financial markets, reflecting his expertise in creating impactful tools.
ICT Liquidity Sweep MAX RETRI (ALERT)Strategy Description: SMC + ICT Reversal Sniper | 5-Min | R2 TP
This strategy applies Smart Money Concepts (SMC) and ICT methodology to identify high-probability reversal trades using a clean, rule-based system designed for the 5-minute timeframe.
⸻
Core Logic:
• Liquidity Sweep: Identifies stop hunts beyond recent swing highs/lows using a configurable lookback window.
• Break of Structure (BOS): Validates a directional shift after the sweep.
• Fixed R2 Risk-Reward: Entry is followed by a 2:1 take-profit target. Stop loss is set at the sweep candle’s high/low.
• No Entry Between 8 PM–12 AM NY Time: Avoids the manipulation-prone and illiquid zone.
• Discreet SL Handling: SL hits close trades silently — no labels or visuals.
⸻
Entry Precision & Timing Notes:
• The strategy may occasionally fire before a confirmed liquidity sweep — this is expected. If a sweep occurs later, you may still re-enter toward equilibrium, with take profit also targeted at equilibrium.
• Alerts or trades that trigger near 9:30 AM NY often align with real direction, but this time can be volatile.
• For more reliable and lower-risk entries, focus on the 1:30 PM to 2:00 PM silver bullet window, which tends to produce cleaner setups with more favorable flow. 🖤
EMA 9/21/50 + VWAP + MACD + RSI Pro [v6]Overview:
A powerful multi-indicator tool combining Exponential Moving Averages (EMA 9, 21, 50), Volume-Weighted Average Price (VWAP), Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI) into a single, easy-to-read system. Designed for traders who want a clean, all-in-one dashboard for trend analysis, momentum confirmation, and overbought/oversold conditions.
Key Features:
1. Triple EMA System (9, 21, 50)
Identifies short-term and medium-term trends.
Bullish Signal: EMA 9 > EMA 21 > EMA 50 (Green Highlight)
Bearish Signal: EMA 9 < EMA 21 < EMA 50 (Red Highlight)
Helps confirm trend direction and potential reversals.
2. VWAP (Volume-Weighted Average Price)
Tracks intraday fair value price based on volume.
Bullish: Price above VWAP (Green)
Bearish: Price below VWAP (Red)
3. MACD (Standard 12, 26, 9 Settings)
Shows momentum shifts.
Bullish: MACD line > Signal line (Green)
Bearish: MACD line < Signal line (Red)
Histogram confirms strength of momentum.
4. RSI (14-Period Default)
Identifies overbought (>70) and oversold (<30) conditions.
Red: Overbought (Potential Reversal)
Green: Oversold (Potential Bounce)
5. Signal Dashboard (Top-Right Table)
Real-time summary of all indicators in one place.
Color-coded for quick interpretation (Green = Bullish, Red = Bearish).
How to Use This Indicator?
✅ Trend Confirmation:
Trade in the direction of EMA alignment (9 > 21 > 50 for uptrends).
Use VWAP as dynamic support/resistance.
✅ Momentum Entries:
Look for MACD crossovers while RSI is not extreme.
Avoid buying when RSI > 70 or selling when RSI < 30 (unless strong trend).
✅ Mean Reversion:
Fade extreme RSI readings (overbought/oversold) when price is at key levels.
Who Is This For?
✔ Swing Traders – EMA + MACD combo for trend-following.
✔ Day Traders – VWAP + EMA for intraday bias.
✔ RSI Traders – Clear overbought/oversold signals.
Settings Customization:
Adjust EMA lengths, RSI periods, and MACD settings as needed.
Toggle VWAP visibility on/off.
Why Use This Script?
📌 All-in-One: No need for multiple indicators cluttering your chart.
📌 Visual Clarity: Color-coded signals for quick decision-making.
📌 Flexible: Works on any timeframe (1M, 5M, 1H, Daily, etc.).
Install now and enhance your trading strategy with a professional-grade multi-indicator system!
Not a financial advice. Use at your own discretion and always apply risk management
Multi Candle Body MapperMulti Candle Body Mapper
Visualize higher-timeframe candle structure within lower timeframes — without switching charts.
This tool maps grouped candle bodies and wicks (e.g., 15min candles on a 5min chart) using precise boxes and lines. Ideal for intraday traders who want to analyze market intent, body bias, and wick rejection in a compressed, organized view.
Features:
Visualize 3, 6, or 12 candle groups (e.g., 15min / 30min / 1H views)
Body box shows bullish/bearish color with adjustable transparency
Wick box shows high-low range with adjustable thickness and color
Dashed line at group close level for market direction hint
Full color customization
Toggle individual elements ON/OFF
Clean overlay – doesn’t interfere with price candles
Great for spotting:
Hidden support/resistance
Momentum buildup
Reversal traps and continuation setups
Keep your chart simple but smarter — all without changing your timeframe.
DTFX Time based range candle box [Wang Indicators]DTFX Time based range candle box
Overview : This indicator highlights HTF Candles in specified timeframe within boxes and extend them until they are mitigated. Allowing traders to use them as zones from which you could find some turn-around or scalp
How does it works ?
Users can setup up to 8 desired timeframe with the hour/minute of the HTF candle
Be carrefull when you chose the time. You must put something coherent with the timeframe (e.g : you can't put 'minutes' = 45 if your timeframe is '1h')
Everyday, the indicator will draw a box around the specified candle for it timeframe
Once the price close above or bellow this candle in the same timeframe, the Zone become "active"
As long as the price doesn't came back into the zone, the retracements will extends
Once the price came back into the zone (in the current timeframe), it stops the expension
Exemple
Here we have those settings :
timeframe : 1 hour
time : 9am
mitigation : 10%
fibs : visible & dashed
The box highlights the 9am 1H candle (9am to 10am)
We now wait for the price to close in the same timeframe (1h here) above or bellow the price
At 11am we close above - the zone is now "active"'
Now we wait for the price to go back in this zone in the current timeframe (here 5min)
12:40am : we put a low above the 10% of the zone -> we stop the retracements, the zone is considered as "mitigated"
Settings
Hour : The hour of the begiging of the candle
Minute : Combined with hour (default 0)
Timeframe : In whichtimeframe we are looking for the candle
% Mitigation : % of the box in wich the price must go back-in in order to "mitigate" the box and stop the expension of the fibs/box (if settings enabled)
Retracements style : Hidden, dashed, dotted or lines for the fibs
Extend Box : extend the box itself until it get mitigated
Number of unmitigated zones : Max unmitigated zone drawed on the chart PER CONFIG
Timezone : Must be set to reflect your needs. (preferably the chart timezone)
How does it helps users ?
Once a Candle is "active" it can be used as a Zone
Fibonnacis levels (30, 50 and 70%) are displayed (if enabled)
Users can customize their apparence and the boxes as they see fit
The 30 - 50 - 70 levels are possible support/resistance that the price tend to bounce of off
You might find some success looking for an entry inside the zone at a level if price gives further confirmations such as a lower time frame flip.
Fair value and MOSShowing the fair value and margin of safety for a Stock.
Works best with 12 months timeframe.
The calculations are based on historical data for multiple years, up to 10 years.
You will see the following as numbers at the indicator line:
- Forward EPS Growth in %
- Forward PE Calculated
- Forward PE Estimated
The two lines will be shown in green if they are above the current price and in red if the price is bellow the lines.
- The upper line shows the fair value of the stock, calculated with 15% (or 4x in 10 years) expected EPS growth for your investment.
- The lower line shows the margin of safety, calculated at 50% of the fair value.
You can adjust the values at "Forward EPS Growth in %" and "Expected future PE" in order to show your fair price and the price with margin of safety.
Multi-SMA Dashboard (10 SMAs)Description:
This script, "Multi-SMA Dashboard (10 SMAs)," creates a dashboard on a TradingView chart to analyze ten Simple Moving Averages (SMAs) of varying lengths. It overlays the chart and displays a table with each SMA’s direction, price position relative to the SMA, and angle of movement, providing a comprehensive trend overview.
How It Works:
1. **Inputs**: Users define lengths for 10 SMAs (default: 5, 10, 20, 50, 100, 150, 200, 250, 300, 350), select a price source (default: close), and customize table appearance and options like angle units (degrees/radians) and debug plots.
2. **SMA Calculation**: Computes 10 SMAs using the `ta.sma()` function with user-specified lengths and price source.
3. **Direction Determination**: The `sma_direction()` function checks each SMA’s trend:
- "Up" if current SMA > previous SMA.
- "Down" if current SMA < previous SMA.
- "Flat" if equal (no strength distinction).
4. **Price Position**: Compares the price source to each SMA, labeling it "Above" or "Below."
5. **Angle Calculation**: Tracks the most recent direction change point for each SMA and calculates its angle (atan of price change over time) in degrees or radians, based on the `showInRadians` toggle.
6. **Table Display**: A 12-column table shows:
- Columns 1-10: SMA name, direction (Up/Down/Flat), Above/Below status, and angle.
- Column 11: Summary of Up, Down, and Flat counts.
- Colors reflect direction (lime for Up/Above, red for Down/Below, white for Flat).
7. **Debug Option**: Optionally plots all SMAs and price for visual verification when `debug_plots_toggle` is enabled.
Indicators Used:
- Simple Moving Averages (SMAs): 10 user-configurable SMAs ranging from short-term (e.g., 5) to long-term (e.g., 350) periods.
The script runs continuously, updating the table on each bar, and overlays the chart to assist traders in assessing multi-timeframe trend direction and momentum without cluttering the view unless debug mode is active.
RSI VWAP POC [Uncle Sam Trading]Category: Oscillators, Volume, Market Profile
Timeframe: Suitable for all timeframes
Markets: Crypto, Forex, Stocks, Commodities
Overview
The RSI VWAP POC indicator is a powerful and innovative oscillator that combines the Relative Strength Index (RSI), Volume-Weighted Average Price (VWAP), and Point of Control (POC) from market profile analysis. Designed to provide traders with clear, high-probability trading signals, this indicator helps you identify key market levels, spot overbought/oversold conditions, and time your entries and exits with precision. Whether you’re a day trader, swing trader, or scalper, this free tool adds significant value to your trading strategy by offering a unique blend of momentum, volume, and market profile insights.
How It Works
This indicator integrates three core components to deliver actionable insights:
RSI (Relative Strength Index): Measures momentum to identify overbought (above 70) and oversold (below 30) conditions, helping you anticipate potential reversals.
VWAP (Volume-Weighted Average Price): Calculates a volume-weighted price benchmark, which is used to compute a more accurate, volume-sensitive RSI. This ensures the indicator reflects true market dynamics.
POC (Point of Control): Derived from market profile analysis, the POC represents the price level with the highest traded volume in a session, acting as a critical support or resistance level.
The indicator plots a smoothed RSI based on VWAP, overlaid with market profile data on a user-defined higher timeframe (default: 4H). The POC is displayed as a red line, with aqua bars indicating the value area where the majority of trading volume occurred. When the RSI crosses the POC, the indicator generates clear buy and sell signals:
Strong Buy (SBU): RSI crosses above the POC in an oversold zone.
Strong Sell (SBD): RSI crosses below the POC in an overbought zone.
Additional features include:
Background colors to highlight bullish (green) or bearish (red) trends.
Shaded zones for overbought (70/60) and oversold (30/40) levels.
Customizable settings to fit your trading style and timeframe.
How This Indicator Adds Value
The RSI VWAP POC indicator offers several key benefits that enhance your trading performance:
High-Probability Signals: By combining RSI, VWAP, and POC, this indicator identifies trades at key market levels where price is likely to react, increasing your win rate.
Improved Timing: Clear buy and sell signals, such as ‘SBU’ and ‘SBD’, help you enter and exit trades at optimal points, maximizing profitability.
Risk Management: Overbought/oversold zones and trend confirmation via background colors help you avoid false signals, protecting your capital.
Versatility: Suitable for all markets (crypto, forex, stocks) and timeframes, making it a valuable tool for traders of all experience levels.
Time Efficiency: The indicator does the heavy lifting by analyzing momentum, volume, and market profile data, allowing you to focus on executing trades.
Real-World Performance Example: On a 1-hour Bitcoin chart with a 4-hour higher timeframe, this indicator identified a strong sell signal on April 6th at 12:00 ($82,000), leading to a 9% drop to $74,600. A subsequent strong buy signal on April 7th at 04:00 ($76,200) captured a 6% rise to $81,200 – a potential 25% profit with 5x leverage if exited at 5%.
How to Use
Add the Indicator: Search for “RSI VWAP POC ” in TradingView’s indicator library and add it to your chart.
Set Your Timeframe: The indicator works on any timeframe but is optimized for a 1-hour chart with a 4-hour higher timeframe (set in the settings).
Interpret Signals:
Look for ‘SBU’ (strong buy) labels when the RSI crosses above the POC in an oversold zone, indicating a potential buying opportunity.
Look for ‘SBD’ (strong sell) labels when the RSI crosses below the POC in an overbought zone, signaling a potential selling opportunity.
Use the background colors (green for bullish, red for bearish) to confirm the trend.
Combine with Your Strategy: Use the indicator alongside your existing analysis (e.g., support/resistance, candlestick patterns) for best results.
Settings and Customization
The indicator is highly customizable to suit your trading needs:
RSI Length (Default: 14): Adjust the sensitivity of the RSI. Use a shorter length (e.g., 10) for scalping, or a longer length (e.g., 20) for smoother signals.
EMA Smoothing Length (Default: 3): Smooths the RSI line. Increase to 5 or 7 for less choppy signals in volatile markets.
Higher Timeframe (Default: 240 minutes): Set to 240 (4 hours) for a 1-hour chart. Adjust based on your chart’s timeframe (e.g., 60 minutes for a 15-minute chart).
Value Area Percentage (Default: 100%): Defines the size of the value area around the POC. Lower to 70% for a tighter focus on key levels.
Overbought/Oversold Thresholds (Defaults: 70/30): Adjust these levels to match market conditions (e.g., 80/20 for trending markets).
Show POC Line (Default: True): Toggle the red POC line on or off.
Show Buy/Sell Signals: Enable ‘Show Strong Breakup Signals’ and ‘Show Strong Breakdown Signals’ to focus on high-probability trades.
Why Choose This Indicator?
The RSI VWAP POC indicator stands out by offering a unique combination of momentum, volume, and market profile analysis in a single, easy-to-use tool. It’s designed to help traders of all levels make informed decisions, reduce risk, and increase profitability. Whether you’re trading Bitcoin, forex pairs, or stocks, this indicator provides the clarity and precision you need to succeed.
