OneTrend EMAThis strategy uses exponential moving averages (EMA) to define market trend direction and employs a dynamic ATR-based threshold adjusted by a custom ADX calculation to generate bullish (blue) and bearish (pink) zones. It enters long positions when the fast EMA exceeds the threshold (blue zone) and exits when it falls below the threshold (pink zone), providing clear, rule-based signals for trend-following trades. Pros include adaptive thresholding that reflects market volatility and trend strength, while cons are potential lag in sideways or choppy markets and susceptibility to whipsaws in volatile conditions.
Đường Trung bình trượt
Multi TF Indicators [KS modded LazBear]Multi TF Indicators
all indicators are showed on table with hi-lighted bull-bear colors
Advanced MACD + MA + RSI + Trend Buy/SellThis advanced indicator combines MACD, dual moving averages, RSI, volume spikes, and a 200 EMA trend filter to generate high-confidence Buy/Sell signals. It aims to reduce false signals by aligning multiple technical conditions:
Liquidity Sweep + OB Trap"A high-precision smart money indicator that detects liquidity sweeps, volume divergence, and order block traps—filtered by trend—to catch false breakouts and sniper reversals."
BTC Swing Trader V2This is a trend-following swing trading strategy that uses Exponential Moving Average (EMA) crossovers to identify entry and exit points for BTC on a 15-minute chart. The goal is to capture short-term price movements (swings) in BTC’s price, aiming for a 0.5-1% profit per trade within a 4-hour window
DavidDias290 EMA StrategyNOT FINAL VERSION! Tested only for the GBPUSD pair, using the 1min chart.
We wait for the price to touch the EMA200 to enter a price rejection.
With a SL of 5Pips and a TP of 15pips, we have a Risk to Reward of 1:3, which gives us an incredible margin to profit in the long term. In all the tests I have developed, I strongly advise using it only in the hours from 00:00 to 2:00 and from 7:00 to 19:00.
EMA ChannelWhat This Indicator Shows:
EMA Center Line
Plots the Exponential Moving Average of the closing price over a user-defined period (length).
Reacts more quickly to price changes than a standard SMA.
Dynamic Channel Bands
Two bands are drawn above and below the EMA.
The distance from the EMA is based on the standard deviation of price over the same period, multiplied by a user-defined width multiplier (mult).
These bands adapt to market volatility — widening during high volatility, narrowing during calm periods.
Channel Fill Area
The space between the upper and lower bands is visually shaded.
Helps quickly identify when price is inside or breaking out of the channel.
Volatility Insights
Since the channel width is based on standard deviation, it indirectly shows market volatility.
Wide channel = high volatility; narrow channel = low volatility.
Potential Trading Zones
Price nearing the upper band may indicate overbought or strong upward pressure.
Price near the lower band might suggest oversold or downward pressure.
Useful for mean reversion or trend continuation strategies depending on your style.
SMA ChannelWhat this indicator does:
Uses a simple moving average (SMA) as the center line.
Calculates the standard deviation of the last N candles.
Builds a channel above and below the center line using the multiplier.
Fills the area between the upper and lower lines
200均线ema200均线指标
自动绘制30分钟、1小时、4小时、1天的均线，并在右下角显示目前均线价格。
EMA200 Moving Average Indicator
Automatically plot the moving averages for 30 - minute, 1 - hour, 4 - hour, and 1 - day timeframes, and display the current moving average prices in the bottom - right corner.
TeeLek KAMAKaufman's Adaptive Moving Average (KAMA)
Kufman is a relatively fast line. When we use it to create an indicator that helps indicate an uptrend or downtrend, it will tell the trend quickly. But the disadvantage is that there will be a lot of false signals.
KAMA Line Multi Timeframe
It is a script that has been further developed to allow us to display KAMA Line in multiple timeframes at the same time.
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คัฟแมน เป็นเส้นที่ค่อนข้างเร็ว เมื่อเราเอามาสร้างเป็น Indicator ที่ช่วยบอก เทรนด์ขึ้นหรือลง จะทำให้มีการบอกเทรนด์ที่เร็ว แตมีข้อด้อยคือ จะมีสัญญาณ false signal เยอะเหมือนกัน
KAMA Line Multi Timeframe
เป็นสคริปที่พัฒนาเพิ่มเติม เพื่อให้เราสามารถแสดง KAMA Line หลายๆ Timeframe พร้อมกันได้
TeeLek-BestPositionBest Buy and Sell Points
This indicator will calculate the best Buy (blue) and Sell (orange) points. The working principle is that the blue point is the point where RSI is Over Sold, the orange point is the point where RSI is Over Bought. After that, we will use the Highest Line 100 and Lowest Line 100 to filter the points another layer.
The appropriate point for buying is
The point where Over Sold occurs and Closes lower than the Lowest Line 100.
The appropriate point for selling is
The point where Over Bought occurs and Closes higher than the Highest Line 100.
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จุดซื้อจุดขายที่ดีที่สุด
อินดิเคเตอร์นี้ จะคำนวณจุดซื้อ (สีฟ้า) และจุดขาย (สีส้ม) ที่ดีที่สุดมาให้ โดยหลักการทำงาน คือ จุดสีฟ้า คือจุดที่ RSI Over Sold จุดสีส้ม คือจุดที่ RSI Over Bought หลังจากนั้นเราจะใช้เส้น Highest Line 100 และ Lowest Line 100 เพื่อกรองจุดอีกชั้นหนึ่ง
จุดที่เหมาะสมกับการซื้อ คือ
จุดที่เกิด Over Sold และ Close ต่ำกว่าเส้น Lowest Line 100
จุดที่เหมาะสมกับการขาย คือ
จุดที่เกิด Over Bought และ Close สูงกว่าเส้น Highest Line 100
MemeSaurus Money Flow CipherThis is a starting point based on common elements in open-source clones and community discussions. Since I don’t have access to the proprietary Market Cipher code, you may need to tweak it further by comparing it to the original indicator’s behavior on a chart.
EMA or SMA Cloud with Third MAThis script will now plot three moving averages on the chart: a fast one, a slow one, and a third one. The area between each pair of moving averages will be filled with a green or red cloud based on whether the first moving average is above or below the second one.
Fast vs Slow MA Cloud: Between the fast and slow moving averages.
Slow vs Third MA Cloud: Between the slow and third moving averages.
You can adjust the lengths of the moving averages and choose between EMA or SMA for all three.
Let me know if this works or if you'd like any further modifications!
KAMA Stdev🚀 Trading View Alert! 🌟
Take your trading strategy to the next level with the KAMA Stdev Indicator! Designed with precision, this script combines the power of KAMA (Kaufman's Adaptive Moving Average) with Standard Deviation Bands for enhanced market insights 📈.
✨ Key Features:
💡 Perfect for traders seeking a dynamic tool for identifying market trends and volatility with ease.
💻 Code licensed under MPL 2.0 and developed by the QuantzTrader!
Check for Price hitting Standard Deviation 3, the price will generally come back to KAMA
NY First Candle Break and RetestStrategy Overview
Session and Time Parameters:
The strategy focuses on the New York trading session, starting at 9:30 AM and lasting for a predefined session length, typically 3 to 4 hours. This timing captures the most active market hours, providing ample trading opportunities.
Strategy Parameters:
Utilizes the Average True Range (ATR) to set dynamic stop-loss levels, ensuring risk is managed according to market volatility.
Employs a reward-to-risk ratio to determine take profit levels, aiming for a balanced approach between potential gains and losses.
Strategy Settings:
Incorporates simple moving averages (EMA) and the Volume Weighted Average Price (VWAP) to identify trend direction and price levels.
Volume confirmation is used to validate breakouts, ensuring trades are based on significant market activity.
Trade Management:
Features a trailing stop mechanism to lock in profits as the trade moves in favor, with multiple take profit levels to secure gains incrementally.
The strategy is designed to handle both long and short positions, adapting to market conditions.
Alert Settings:
Provides alerts for key events such as session start, breakout, retest, and entry signals, helping traders stay informed and act promptly.
Visual cues on the chart highlight entry and exit points, making it easier for beginners to follow the strategy.
This strategy is particularly suited for the current volatile market environment, where simplicity and clear guidelines can help beginner traders navigate the complexities of trading. It emphasizes risk management and uses straightforward indicators to make informed trading decisions.
I put together this Trading View scalping strategy for futures markets with some help from Claude AI. Shoutout to everyone who gave me advice along the way—I really appreciate it! I’m sure there’s room for improvement, so feel free to share your thoughts… just go easy on me. :)
EMA or SMA CloudHow it works:
You can choose between EMA or SMA for all three moving averages using the maType input.
The clouds are filled based on the relationship between the moving averages:
Green cloud: The first MA is above the second MA.
Red cloud: The first MA is below the second MA.
How to Use:
Copy the code above.
Open TradingView.
Go to "Pine Editor" at the bottom of the screen.
Paste the code and click "Add to Chart."
In the indicator settings, you’ll be able to choose whether to use EMA or SMA for all three moving averages, and the chart will show the moving averages along with the corresponding clouds.
EMA-Based Squeeze Dynamics (Gap Momentum & EWMA Projection)EMA-Based Squeeze Dynamics (Gap Momentum & EWMA Projection)
🚨 Main Utility: Early Squeeze Warning
The primary function of this indicator is to warn traders early when the market is approaching a "squeeze"—a tightening condition that often precedes significant moves or regime shifts. By visually highlighting areas of increasing tension, it helps traders anticipate potential volatility and prepare accordingly. This is intended to be a statistically and psychologically grounded replacement of so-called "fib-time-zones," which are overly-deterministic and subjective.
📌 Overview
The EMA-Based Squeeze Dynamics indicator projects future regime shifts (such as golden and death crosses) using exponential moving averages (EMAs). It employs historical interval data and current market conditions to dynamically forecast when the critical EMAs (50-period and 200-period) will reconverge, marking likely trend-change points.
This indicator leverages two core ideas:
Behavioral finance theory: Traders often collectively anticipate popular EMA crossovers, creating a self-fulfilling prophecy (normative social influence), similar to findings from Solomon Asch’s conformity experiments.
Bayesian-like updates: It utilizes historical crossover intervals as a prior, dynamically updating expectations based on evolving market data, ensuring its signals remain objectively grounded in actual market behavior.
⚙️ Technical & Mathematical Explanation
1. EMA Calculations and Regime Definitions
The indicator uses three EMAs:
Fast (9-period): Represents short-term price movement.
Medial (50-period): Indicates medium-term trend direction.
Slow (200-period): Defines long-term market sentiment.
Regime States:
Bullish: 50 EMA is above the 200 EMA.
Bearish: 50 EMA is below the 200 EMA.
A shift between these states triggers visual markers (arrows and labels) directly on the chart.
2. Gap Dynamics and Historical Intervals
At each crossover:
The indicator records the gap (distance) between the 50 and 200 EMAs.
It tracks the historical intervals between past crossovers.
An Exponentially Weighted Moving Average (EWMA) of these intervals is calculated, weighting recent intervals more heavily, dynamically updating expectations.
Important note:
After every regime shift, the projected crossover line resets its calculation. This reset is visually evident as the projection line appears to move further away after each regime change, temporarily "repelled" until the EMAs begin converging again. This ensures projections remain realistic, grounded in actual EMA convergence, and prevents overly optimistic forecasts immediately after a regime shift.
3. Gap Momentum & Adaptive Scaling
The indicator measures how quickly or slowly the gap between EMAs is changing ("gap momentum") and adjusts its forecast accordingly:
If the gap narrows rapidly, a crossover becomes more imminent.
If the gap widens, the next crossover is pushed further into the future.
The "gap factor" dynamically scales the projection based on recent gap momentum, bounded between reasonable limits (0.7–1.3).
4. Squeeze Ratio & Background Color (Visual Cues)
A "squeeze ratio" is computed when market conditions indicate tightening:
In a bullish regime, if the fast EMA is below the medial EMA (price pulling back towards long-term support), the squeeze ratio increases.
In a bearish regime, if the fast EMA rises above the medial EMA (price rallying into long-term resistance), the squeeze ratio increases.
What the Background Colors Mean:
Red Background: Indicates a bullish squeeze—price is compressing downward, hinting a bullish reversal or continuation breakout may occur soon.
Green Background: Indicates a bearish squeeze—price is compressing upward, suggesting a bearish reversal or continuation breakout could soon follow.
Opacity Explanation:
The transparency (opacity) of the background indicates the intensity of the squeeze:
High Opacity (solid color): Strong squeeze, high likelihood of imminent volatility or regime shift.
Low Opacity (faint color): Mild squeeze, signaling early stages of tightening.
Thus, more vivid colors serve as urgent visual warnings that a squeeze is rapidly intensifying.
5. Projected Next Crossover and Pseudo Crossover Mechanism
The indicator calculates an estimated future bar when a crossover (and thus, regime shift) is expected to occur. This calculation incorporates:
Historical EWMA interval.
Current squeeze intensity.
Gap momentum.
A dynamic penalty based on divergence from baseline conditions.
The "Pseudo Crossover" Explained:
A key adaptive feature is the pseudo crossover mechanism. If price action significantly deviates from the projected crossover (for example, if price stays beyond the projected line longer than expected), the indicator acknowledges the projection was incorrect and triggers a "pseudo crossover" event. Essentially, this acts as a reset, updating historical intervals with a weighted adjustment to recalibrate future predictions. In other words, if the indicator’s initial forecast proves inaccurate, it recognizes this quickly, resets itself, and tries again—ensuring it remains responsive and adaptive to actual market conditions.
🧠 Behavioral Theory: Normative Social Influence
This indicator is rooted in behavioral finance theory, specifically leveraging normative social influence (conformity). Traders commonly watch EMA signals (especially the 50 and 200 EMA crossovers). When traders collectively anticipate these signals, they begin trading ahead of actual crossovers, effectively creating self-fulfilling prophecies—similar to Solomon Asch’s famous conformity experiments, where individuals adopted group behaviors even against direct evidence.
This behavior means genuine regime shifts (actual EMA crossovers) rarely occur until EMAs visibly reconverge due to widespread anticipatory trading activity. The indicator quantifies these dynamics by objectively measuring EMA convergence and updating projections accordingly.
📊 How to Use This Indicator
Monitor the background color and opacity as primary visual cues.
A strongly colored background (solid red/green) is an early alert that a squeeze is intensifying—prepare for potential volatility or a regime shift.
Projected crossover lines give a dynamic target bar to watch for trend reversals or confirmations.
After each regime shift, expect a reset of the projection line. The line may seem initially repelled from price action, but it will recalibrate as EMAs converge again.
Trust the pseudo crossover mechanism to automatically recalibrate the indicator if its original projection misses.
🎯 Why Choose This Indicator?
Early Warning: Visual squeeze intensity helps anticipate market breakouts.
Behaviorally Grounded: Leverages real trader psychology (conformity and anticipation).
Objective & Adaptive: Uses real-time, data-driven updates rather than static levels or subjective analysis.
Easy to Interpret: Clear visual signals (arrows, labels, colors) simplify trading decisions.
Self-correcting (Pseudo Crossovers): Quickly adjusts when initial predictions miss, maintaining accuracy over time.
Summary:
The EMA-Based Squeeze Dynamics Indicator combines behavioral insights, dynamic Bayesian-like updates, intuitive visual cues, and a self-correcting pseudo crossover feature to offer traders a reliable early warning system for market squeezes and impending regime shifts. It transparently recalibrates after each regime shift and automatically resets whenever projections prove inaccurate—ensuring you always have an adaptive, realistic forecast.
Whether you're a discretionary trader or algorithmic strategist, this indicator provides a powerful tool to navigate market volatility effectively.
Happy Trading! 📈✨
Half Causal EstimatorOverview
The Half Causal Estimator is a specialized filtering method that provides responsive averages of market variables (volume, true range, or price change) with significantly reduced time delay compared to traditional moving averages. It employs a hybrid approach that leverages both historical data and time-of-day patterns to create a timely representation of market activity while maintaining smooth output.
Core Concept
Traditional moving averages suffer from time lag, which can delay signals and reduce their effectiveness for real-time decision making. The Half Causal Estimator addresses this limitation by using a non-causal filtering method that incorporates recent historical data (the causal component) alongside expected future behavior based on time-of-day patterns (the non-causal component).
This dual approach allows the filter to respond more quickly to changing market conditions while maintaining smoothness. The name "Half Causal" refers to this hybrid methodology—half of the data window comes from actual historical observations, while the other half is derived from time-of-day patterns observed over multiple days. By incorporating these "future" values from past patterns, the estimator can reduce the inherent lag present in traditional moving averages.
How It Works
The indicator operates through several coordinated steps. First, it stores and organizes market data by specific times of day (minutes/hours). Then it builds a profile of typical behavior for each time period. For calculations, it creates a filtering window where half consists of recent actual data and half consists of expected future values based on historical time-of-day patterns. Finally, it applies a kernel-based smoothing function to weight the values in this composite window.
This approach is particularly effective because market variables like volume, true range, and price changes tend to follow recognizable intraday patterns (they are positive values without DC components). By leveraging these patterns, the indicator doesn't try to predict future values in the traditional sense, but rather incorporates the average historical behavior at those future times into the current estimate.
The benefit of using this "average future data" approach is that it counteracts the lag inherent in traditional moving averages. In a standard moving average, recent price action is underweighted because older data points hold equal influence. By incorporating time-of-day averages for future periods, the Half Causal Estimator essentially shifts the center of the filter window closer to the current bar, resulting in more timely outputs while maintaining smoothing benefits.
Understanding Kernel Smoothing
At the heart of the Half Causal Estimator is kernel smoothing, a statistical technique that creates weighted averages where points closer to the center receive higher weights. This approach offers several advantages over simple moving averages. Unlike simple moving averages that weight all points equally, kernel smoothing applies a mathematically defined weight distribution. The weighting function helps minimize the impact of outliers and random fluctuations. Additionally, by adjusting the kernel width parameter, users can fine-tune the balance between responsiveness and smoothness.
The indicator supports three kernel types. The Gaussian kernel uses a bell-shaped distribution that weights central points heavily while still considering distant points. The Epanechnikov kernel employs a parabolic function that provides efficient noise reduction with a finite support range. The Triangular kernel applies a linear weighting that decreases uniformly from center to edges. These kernel functions provide the mathematical foundation for how the filter processes the combined window of past and "future" data points.
Applicable Data Sources
The indicator can be applied to three different data sources: volume (the trading volume of the security), true range (expressed as a percentage, measuring volatility), and change (the absolute percentage change from one closing price to the next).
Each of these variables shares the characteristic of being consistently positive and exhibiting cyclical intraday patterns, making them ideal candidates for this filtering approach.
Practical Applications
The Half Causal Estimator excels in scenarios where timely information is crucial. It helps in identifying volume climaxes or diminishing volume trends earlier than conventional indicators. It can detect changes in volatility patterns with reduced lag. The indicator is also useful for recognizing shifts in price momentum before they become obvious in price action, and providing smoother data for algorithmic trading systems that require reduced noise without sacrificing timeliness.
When volatility or volume spikes occur, conventional moving averages typically lag behind, potentially causing missed opportunities or delayed responses. The Half Causal Estimator produces signals that align more closely with actual market turns.
Technical Implementation
The implementation of the Half Causal Estimator involves several technical components working together. Data collection and organization is the first step—the indicator maintains a data structure that organizes market data by specific times of day. This creates a historical record of how volume, true range, or price change typically behaves at each minute/hour of the trading day.
For each calculation, the indicator constructs a composite window consisting of recent actual data points from the current session (the causal half) and historical averages for upcoming time periods from previous sessions (the non-causal half). The selected kernel function is then applied to this composite window, creating a weighted average where points closer to the center receive higher weights according to the mathematical properties of the chosen kernel. Finally, the kernel weights are normalized to ensure the output maintains proper scaling regardless of the kernel type or width parameter.
This framework enables the indicator to leverage the predictable time-of-day components in market data without trying to predict specific future values. Instead, it uses average historical patterns to reduce lag while maintaining the statistical benefits of smoothing techniques.
Configuration Options
The indicator provides several customization options. The data period setting determines the number of days of observations to store (0 uses all available data). Filter length controls the number of historical data points for the filter (total window size is length × 2 - 1). Filter width adjusts the width of the kernel function. Users can also select between Gaussian, Epanechnikov, and Triangular kernel functions, and customize visual settings such as colors and line width.
These parameters allow for fine-tuning the balance between responsiveness and smoothness based on individual trading preferences and the specific characteristics of the traded instrument.
Limitations
The indicator requires minute-based intraday timeframes, securities with volume data (when using volume as the source), and sufficient historical data to establish time-of-day patterns.
Conclusion
The Half Causal Estimator represents an innovative approach to technical analysis that addresses one of the fundamental limitations of traditional indicators: time lag. By incorporating time-of-day patterns into its calculations, it provides a more timely representation of market variables while maintaining the noise-reduction benefits of smoothing. This makes it a valuable tool for traders who need to make decisions based on real-time information about volume, volatility, or price changes.
Dskyz Adaptive Futures Elite (DAFE)Dskyz Adaptive Futures Edge (DAFE)
imgur.com
A Dynamic Futures Trading Strategy
DAFE adapts to market volatility and price action using technical indicators and advanced risk management. It’s built for high-stakes futures trading (e.g., MNQ, BTCUSDT.P), offering modular logic for scalpers and swing traders alike.
Key Features
Adaptive Moving Averages
Dynamic Logic: Fast and slow SMAs adjust lengths via ATR, reacting to momentum shifts and smoothing in calm markets.
Signals: Long entry on fast SMA crossing above slow SMA with price confirmation; short on cross below.
RSI Filtering (Optional)
Momentum Check: Confirms entries with RSI crossovers (e.g., above oversold for longs). Toggle on/off with custom levels.
Fine-Tuning: Adjustable lookback and thresholds (e.g., 60/40) for precision.
Candlestick Pattern Recognition
Eng|Enhanced Detection: Identifies strong bullish/bearish engulfing patterns, validated by volume and range strength (vs. 10-period SMA).
Conflict Avoidance: Skips trades if both patterns appear in the lookback window, reducing whipsaws.
Multi-Timeframe Trend Filter
15-Minute Alignment: Syncs intrabar trades with 15-minute SMA trends; optional for flexibility.
Dollar-Cost Averaging (DCA) New!
Scaling: Adds up to a set number of entries (e.g., 4) on pullbacks/rallies, spaced by ATR multiples.
Control: Caps exposure and resets on exit, enhancing trend-following potential.
Trade Execution & Risk Management
Entry Rules: Prioritizes moving averages or patterns (user choice), with volume, volatility, and time filters.
Stops & Trails:
Initial Stop: ATR-based (2–3.5x, volatility-adjusted).
Trailing Stop: Locks profits with configurable ATR offset and multiplier.
Discipline
Cooldown: Pauses post-exit (e.g., 0–5 minutes).
Min Hold: Ensures trades last a set number of bars (e.g., 2–10).
Visualization & Tools
Charts: Overlays MAs, stops, and signals; trend shaded in background.
Dashboard: Shows position, P&L, win rate, and more in real-time.
Debugging: Logs signal details for optimization.
Input Parameters
Parameter Purpose Suggested Use
Use RSI Filter - Toggle RSI confirmation *Disable 4 price-only
trading
RSI Length - RSI period (e.g., 14) *7–14 for sensitivity
RSI Overbought/Oversold - Adjust for market type *Set levels (e.g., 60/40)
Use Candlestick Patterns - Enables engulfing signals *Disable for MA focus
Pattern Lookback - Pattern window (e.g., 19) *10–20 bars for balance
Use 15m Trend Filter - Align with 15-min trend *Enable for trend trades
Fast/Slow MA Length - Base MA lengths (e.g., 9/19) *10–25 / 30–60 per
timeframe
Volatility Threshold - Filters volatile spikes *Max ATR/close (e.g., 1%)
Min Volume - Entry volume threshold *Avoid illiquid periods
(e.g., 10)
ATR Length - ATR period (e.g., 14) *Standard volatility
measure
Trailing Stop ATR Offset - Trail distance (e.g., 0.5) *0.5–1.5 for tightness
Trailing Stop ATR Multi - Trail multiplier (e.g., 1.0) *1–3 for trend room
Cooldown Minutes - Post-exit pause (e.g., 0–5) *Prevents overtrading
Min Bars to Hold - Min trade duration (e.g., 2) *5–10 for intraday
Trading Hours - Active window (e.g., 9–16) *Focus on key sessions
Use DCA - Toggle DCA *Enable for scaling
Max DCA Entries - Cap entries (e.g., 4) *Limit risk exposure
DCA ATR Multiplier Entry spacing (e.g., 1.0) *1–2 for wider gaps
Compliance
Realistic Testing: Fixed quantities, capital, and slippage for accurate backtests.
Transparency: All logic is user-visible and adjustable.
Risk Controls: Cooldowns, stops, and hold periods ensure stability.
Flexibility: Adapts to various futures and timeframes.
Summary
DAFE excels in volatile futures markets with adaptive logic, DCA scaling, and robust risk tools. Currently in prop account testing, it’s a powerful framework for precision trading.
Caution
DAFE is experimental, not a profit guarantee. Futures trading risks significant losses due to leverage. Backtest, simulate, and monitor actively before live use. All trading decisions are your responsibility.
M2 Global Liquidity Index (108-day delay)This indicator tracks global liquidity by summing the M2 money supply of the largest economies (China, USA, Europe, Japan, and the UK), adjusted to USD via exchange rates. By delaying the indicator by 108 days, it reveals how global monetary expansion or contraction leads Bitcoin’s price action.
Recently, during the last cycle, Bitcoin has been closely mirroring the movements of global liquidity, rising as liquidity increases and pulling back during contractions. This tool offers powerful macroeconomic insights for those trading or accumulating BTC.
TestMA Candle ColorTestMA Candle Color. To find MA breakouts.Once a green candle above MA - buy signal. Sell signal when the day green candle breaks down.
Fibonacci Counter-Trend TradingOverview:
The Fibonacci Counter-Trend Trading strategy is designed to capitalize on price reversals by utilizing Fibonacci levels calculated from the standard deviation of price movements. This strategy opens a sell order when the closing price crosses above a specified upper Fibonacci level and a buy order when the closing price crosses below a specified lower Fibonacci level. By leveraging the principles of Fibonacci retracement and volatility, this strategy aims to identify potential reversal points in the market.
How It Works:
Fibonacci Levels Calculation:
The strategy calculates upper and lower Fibonacci levels based on the standard deviation of the price over a specified moving average length. These levels are derived from the Fibonacci sequence, which is widely used in technical analysis to identify potential support and resistance levels.
The upper levels are calculated by adding specific Fibonacci ratios (0.236, 0.382, 0.5, 0.618, 0.764, and 1.0) multiplied by the standard deviation to the basis (the volume-weighted moving average).
The lower levels are calculated by subtracting the same Fibonacci ratios multiplied by the standard deviation from the basis.
Trade Entry Rules:
Sell Order: A sell order is triggered when the closing price crosses above the selected upper Fibonacci level. This indicates a potential reversal point where the price may start to decline.
Buy Order: A buy order is initiated when the closing price crosses below the selected lower Fibonacci level. This suggests a potential reversal point where the price may begin to rise.
Trade Management:
The strategy includes stop-losses based on the Fibonacci levels to protect against adverse price movements.
How to Use:
Users can customize the moving average length and the multiplier for the standard deviation to suit their trading preferences and market conditions.
The strategy can be applied to various financial instruments, including stocks, forex, and cryptocurrencies, making it versatile for different trading environments.
Pros:
The Fibonacci Counter-Trend Trading strategy combines the mathematical principles of the Fibonacci sequence with the statistical measure of standard deviation, providing a unique approach to identifying potential market reversals.
This strategy is particularly useful in volatile markets where price swings can lead to significant trading opportunities.
The use of Fibonacci levels can help traders identify key support and resistance areas, enhancing decision-making.
Cons:
The strategy may generate false signals in choppy or sideways markets, leading to potential losses if the price does not reverse as anticipated.
Relying solely on Fibonacci levels without considering other technical indicators or market conditions may result in missed opportunities or increased risk.
The effectiveness of the strategy can vary depending on the chosen parameters (e.g., moving average length and standard deviation multiplier), requiring users to spend time optimizing these settings for different market conditions.
As with any counter-trend strategy, there is a risk of significant drawdowns during strong trending markets, where the price continues to move in one direction without reversing.
By understanding the mechanics of the Fibonacci Counter-Trend Trading strategy, along with its pros and cons, traders can effectively implement it in their trading routines and potentially enhance their trading performance.
MTF EMA CloudsThis indicator creates up to 3 configurable EMA clouds
Each cloud uses a fast ema and a slow ema.
Works as follows:
fast ema > slow ema : green
slow ema > fast ema : red
This also allows you to select a custom timeframe for each cloud (only higher timeframes work) and applies a multiplier to show the cloud from that timeframe on the current chart.
