Black Tie FibonacciThis indicator plots Yesterday’s, Last Week’s, or Last Month’s Open/Close/High/Low levels, plus Fibonacci retracements and the Optimal Trade Entry (OTE) zone. It includes alerts for each key level, making it perfect for trading reactions on lower timeframes (15m-5m).
The goal of all my indicators is to save you time on manual charting—because making money shouldn’t mean being glued to a screen.
Hope you enjoy it,
Educational
Z-Score Weighted Trend System I [InvestorUnknown]The Z-Score Weighted Trend System I is an advanced and experimental trading indicator designed to utilize a combination of slow and fast indicators for a comprehensive analysis of market trends. The system is designed to identify stable trends using slower indicators while capturing rapid market shifts through dynamically weighted fast indicators. The core of this indicator is the dynamic weighting mechanism that utilizes the Z-score of price , allowing the system to respond effectively to significant market movements.
Dynamic Z-Score-Based Weighting System
The Z-Score Weighted Trend System I utilizes the Z-score of price to assign weights dynamically to fast indicators. This mechanism is designed to capture rapid market shifts at potential turning points, providing timely entry and exit signals.
Traders can choose from two primary weighting mechanisms:
Threshold-Based Weighting: The fast indicators are given weight only when the absolute Z-score exceeds a user-defined threshold. Below this threshold, fast indicators have no impact on the final signal.
Continuous Weighting: By setting the threshold to zero, fast indicators always contribute to the final signal, regardless of Z-score levels. However, this increases the likelihood of false signals during ranging or low-volatility markets
// Calculate weight for Fast Indicators based on Z-Score (Slow Indicator weight is kept to 1 for simplicity)
f_zscore_weights(series float z, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(z) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(z))
slow_weight := 1
Choice of Z-Score Normalization
Traders have the flexibility to select different Z-score processing methods to better suit their trading preferences:
Raw Z-Score or Moving Average: Traders can opt for either the raw Z-score or a moving average of the Z-score to smooth out fluctuations.
Normalized Z-Score (ranging from -1 to 1) or Z-Score Percentile: The normalized Z-score is simply the raw Z-score divided by 3, while the Z-score percentile utilizes a normal distribution for transformation.
f_zscore_perc(series float zscore_src, simple int zscore_len, simple string zscore_a, simple string zscore_b, simple string ma_type, simple int ma_len) =>
z = (zscore_src - ta.sma(zscore_src, zscore_len)) / ta.stdev(zscore_src, zscore_len)
zscore = switch zscore_a
"Z-Score" => z
"Z-Score MA" => ma_type == "EMA" ? (ta.ema(z, ma_len)) : (ta.sma(z, ma_len))
output = switch zscore_b
"Normalized Z-Score" => (zscore / 3) > 1 ? 1 : (zscore / 3) < -1 ? -1 : (zscore / 3)
"Z-Score Percentile" => (f_percentileFromZScore(zscore) - 0.5) * 2
output
Slow and Fast Indicators
The indicator uses a combination of slow and fast indicators:
Slow Indicators (constant weight) for stable trend identification: DMI (Directional Movement Index), CCI (Commodity Channel Index), Aroon
Fast Indicators (dynamic weight) to identify rapid trend shifts: ZLEMA (Zero-Lag Exponential Moving Average), IIRF (Infinite Impulse Response Filter)
Each indicator is calculated using for-loop methods to provide a smoothed and averaged view of price data over varying lengths, ensuring stability for slow indicators and responsiveness for fast indicators.
Signal Calculation
The final trading signal is determined by a weighted combination of both slow and fast indicators. The slow indicators provide a stable view of the trend, while the fast indicators offer agile responses to rapid market movements. The signal calculation takes into account the dynamic weighting of fast indicators based on the Z-score:
// Calculate Signal (as weighted average)
float sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
The indicator features a detailed backtesting mode, allowing traders to compare the effectiveness of their selected settings against a traditional Buy & Hold strategy. The backtesting provides:
Equity calculation based on signals generated by the indicator.
Performance metrics comparing Buy & Hold metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations, Sharpe, Sortino, and Omega Ratios
// Calculate Performance Metrics
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback), 4)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na), 4)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na), 4)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round((mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
sortino_ratio = math.round((mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1))* 100, 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
//}
Calibration Mode
A Calibration Mode is included for traders to focus on individual indicators, helping them fine-tune their settings without the influence of other components. In Calibration Mode, the user can visualize each indicator separately, making it easier to adjust parameters.
Alerts
The indicator includes alerts for long and short signals when the indicator changes direction, allowing traders to set automated notifications for key market events.
// Alert Conditions
alertcondition(long_alert, "LONG (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (Z-Score Weighted Trend System)", "Z-Score Weighted Trend System flipped ⬇Short⬇")
Important Note:
The default settings of this indicator are not optimized for any particular market condition. They are generic starting points for experimentation. Traders are encouraged to use the calibration tools and backtesting features to adjust the system to their specific trading needs.
The results generated from the backtest are purely historical and are not indicative of future results. Market conditions can change, and the performance of this system may differ under different circumstances. Traders and investors should exercise caution and conduct their own research before using this indicator for any trading decisions.
30D Vs 90D Historical VolatilityVolatility equals risk for an underlying asset's price meaning bullish volatility is bearish for prices while bearish volatility is bullish. This compares 30-Day Historical Volatility to 90-Day Historical Volatility.
When the 30-Day crosses under the 90-day, this is typically when asset prices enter a bullish trend.
Conversely, When the 30-Day crosses above the 90-Day, this is when asset prices enter a bearish trend.
Peaks in volatility are bullish divergences while troughs are bearish divergences.
4AM-5AM BRT HighlighterThe 4AM-5AM BRT Highlighter is a simple yet effective tool designed to visually mark your preferred trading time on the chart. It highlights the period between 4:00 AM and 5:00 AM Brazilian Time (BRT/UTC-3) by default, helping you stay focused and aware of your prime trading window.
Key Features:
Clear Visual Highlight: Colors the background of your chart during the chosen timeframe, making it easy to see when your trading session starts and ends.
Customizable Colors: Easily adjust the highlight color and transparency to suit your visual preferences.
Accurate Time Conversion: Automatically accounts for Brazilian Time (BRT), ensuring the highlight appears correctly no matter your chart’s default timezone.
Whether you're trading currencies, metals, indexes, or cryptocurrencies, this indicator helps you maintain focus during your dedicated trading hour by clearly marking your active period on the chart.
Bollinger strat gold h1 signalThis unique script integrates Bollinger Bands and Fibonacci retracement levels to provide traders with actionable buy and sell signals, along with independent management of positions through distinct visual boxes on the chart.
Key Features:
Bollinger Bands: Calculated using a specified moving average length and multiplier, these bands highlight potential price extremes, assisting traders in identifying overbought and oversold conditions.
Fibonacci Levels: The script calculates critical Fibonacci retracement levels based on the highest and lowest prices over a defined length. These levels serve as potential entry points and targets for take-profit.
Dynamic Position Management: For each buy or sell signal, a box is created to visually track the entry, stop-loss, and take-profit levels. This clear visual representation allows for straightforward trade management.
Success Rate Calculation: The indicator evaluates the performance of the last seven positions, displaying the success rate directly on the chart. This feature helps traders assess the effectiveness of their strategy in real-time.
Usage Instructions:
Signal Generation: The script automatically generates buy or sell signals when the price crosses the Bollinger Bands, indicating possible trading opportunities.
Tracking Positions: Each trade is represented by a box that updates dynamically, providing a visual summary of your trade performance.
Success Rate Overview: The displayed success rate allows traders to quickly evaluate their trading performance based on the last seven trades, aiding in decision-making.
Customizable Parameters:
Bollinger Length: Adjust the period used for calculating the Bollinger Bands.
Bollinger Multiplier: Set the sensitivity of the bands to market movements.
Fibonacci Length: Define the period for calculating Fibonacci retracement levels.
Important Note: This script is compatible with any trading asset and can be applied across various timeframes. Users are encouraged to conduct thorough backtesting on historical data to validate its effectiveness before utilizing it for live trading.
Buy and Sell Signals Based on SMI {K28}Buy/Sell Signals Based on SMI
This indicator provides buy and sell signals based on the Stochastic Momentum Index (SMI) to assist traders in identifying potential entry and exit points in the market. Here’s how to effectively use this indicator:
Usage Instructions:
Signal Interpretation:
No signal is 100% guaranteed
Green Labels: Indicate strong buy signals when the SMI crosses above its EMA, especially if the candle is green (closing price higher than opening price).
Red Labels: Indicate strong sell signals when the SMI crosses below its EMA.
Cautious Signals:
Blue Buy Labels: These buy signals appear when the SMI is in a cautious zone (between -20 and 20). They may not be as reliable, so confirm these signals with other indicators before acting.
Yellow Sell Labels: These buy signals appear when the SMI is in a cautious zone (between -20 and 20). They may not be as reliable, so confirm these signals with other indicators before acting.
Gray Buy and Sell Labels: Indicate potential false signals (when the SMI is overbought or oversold). Use other confirmation indicators to verify these signals.
Trade Strategy:
This indicator is designed for traders looking to make small, consistent profits. Focus on executing more trades rather than waiting for larger price movements.
Be mindful that the indicator may yield frequent signals, so it's essential to maintain discipline and only take trades that meet your criteria for confirmation.
Important Notes:
Caution with Signals: Always exercise caution when acting on blue or gray labels. These may indicate less reliable signals, so it's crucial to confirm with additional indicators.
No Perfect Indicator: Please remember that no trading indicator is perfect. Use this indicator at your own risk, and consider incorporating risk management strategies into your trading plan.
Conclusion:
By employing this SMI indicator, you can enhance your trading strategy focused on generating small, consistent profits through frequent trades. However, always verify signals and stay aware of market conditions to optimize your trading performance.
Daily Range Position Sizer// ENGLISH, GERMAN BELOW
Daily Range Position Sizer
A versatile indicator that helps traders calculate position sizes based on daily high/low levels with flexible session handling.
Features:
- Displays daily high and low levels with broken line style for clear visibility
- Calculates position sizes for both long and short setups based on your defined risk amount
- Flexible session dealing with optional pre-market inclusion
- Real-time position size updates based on current price
- Clearly visible info box showing position sizes
Settings:
- Risk Amount ($): Define your desired risk amount in dollars
- Include Pre-market: Toggle between regular session only (9:30-16:00 ET) or full day including pre-market
- X-Offset: Adjust the horizontal position of the info box
Usage:
1. Set your desired risk amount
2. Choose whether to include pre-market data
3. Monitor daily ranges and corresponding position sizes
4. Use the automatically calculated position sizes for your trades
Note: This indicator is for educational and informational purposes only. Always verify calculations and manage your risk appropriately.
Contributions and feedback are welcome!
// GERMAN
Tageshoch/-Tief & Positionsgröße
Ein vielseitiger Indikator, der Tradern bei der Berechnung von Positionsgrößen auf der Basis von Tageshochs und -tiefs hilft und eine flexible Handhabung der Sitzungen ermöglicht.
Eigenschaften:
- Zeigt tägliche Höchst- und Tiefststände mit gestrichelten Linien für eine klare Sichtbarkeit an
- Berechnet Positionsgrößen für Long- und Short-Setups auf der Grundlage des von Ihnen definierten Risikobetrags
- Flexibles Session-Handling mit optionaler Einbeziehung des vorbörslichen Handels
- Aktualisierung der Positionsgröße in Echtzeit auf Basis des aktuellen Kurses
- Deutlich sichtbare Infobox mit Positionsgrößen
Einstellungen:
- Risikobetrag ($): Definieren Sie Ihren gewünschten Risikobetrag in Dollar
- Vorbörslich einbeziehen: Schalten Sie um zwischen nur regulärer Sitzung (9:30-16:00 ET) oder ganztägig einschließlich Vormarkt
- X-Absatz: Passen Sie die horizontale Position der Infobox an
Verwendung:
1. Legen Sie den gewünschten Risikobetrag fest
2. Wählen Sie, ob vorbörsliche Daten einbezogen werden sollen
3. Überwachen Sie die täglichen Bandbreiten und die entsprechenden Positionsgrößen
4. Verwenden Sie die automatisch berechneten Positionsgrößen für Ihre Trades
Hinweis: Dieser Indikator ist nur für Lehr- und Informationszwecke gedacht. Überprüfen Sie stets die Berechnungen und steuern Sie Ihr Risiko angemessen.
Beiträge und Feedback sind willkommen!
Smart Money Setup 07 [TradingFinder] Liquidity Hunts & Minor OB🔵 Introduction
The Smart Money Concept relies on analyzing market structure, tracking liquidity flows, and identifying order blocks. Research indicates that traders who apply these methods can improve their accuracy in predicting market movements by up to 30%.
These elements allow traders to understand the behavior of market makers, including banks and large financial institutions, which have the ability to influence price movements and shape major market trends. By recognizing how these entities operate, traders can align their strategies with Smart Money actions and better anticipate shifts in the market.
Smart Money typically enters the market at points of high liquidity where trading opportunities are more attractive. By following these liquidity flows, professional traders can position themselves at market reversal points, leading to profitable trades.
The Smart Money Setup 07 indicator has been specifically designed to detect these complex patterns. Using advanced algorithms, this indicator automatically identifies both bullish and bearish trading setups, assisting traders in discovering hidden market opportunities.
As a powerful technical analysis tool, the Smart Money Setup indicator helps predict the actions of major market participants and highlights optimal entry and exit points. Essentially, this tool enables traders to act like institutional investors and market makers, making the most of price fluctuations in their favor.
Ultimately, the Smart Money Setup 07 indicator transforms complex technical analysis into a simple and practical tool. By detecting order blocks and liquidity zones, this tool helps traders execute their strategies with greater precision, leading to more informed and successful trading decisions.
🟣 Bullish Setup
🟣 Bearish Setup
🔵 How to Use
One of the key strengths of the Smart Money Setup 07 indicator is its ability to accurately identify order blocks and analyze liquidity flows. Order blocks represent areas where large buy or sell orders are placed by Smart Money investors, which often indicate key reversal points in the market. Traders can use these order blocks to pinpoint potential entry and exit opportunities.
The Smart Money Setup indicator detects and visually displays these order blocks on the chart, helping traders identify the best zones to enter or exit trades. Since these zones are frequently used by large institutional investors, following these blocks allows traders to capitalize on price fluctuations and trade with confidence.
🟣 Bullish Smart Money Setup
A Bullish Smart Money Setup forms when the market creates Higher Lows and Higher Highs. In this situation, the indicator analyzes pivot points, liquidity flows, and order blocks to identify buy opportunities. Liquidity points in these setups indicate areas where Smart Money is likely to enter long positions.
In the bullish setup image, multiple Higher Lows and Higher Highs are formed. The green zone represents a Bullish Order Block, signaling traders to enter a long trade. The Smart Money Setup indicator displays a green arrow, indicating a high-probability upward price movement from this liquidity zone.
🟣 Bearish Smart Money Setup
A Bearish Smart Money Setup occurs when the market structure shows Lower Highs and Lower Lows, indicating weakness in price. The indicator identifies these patterns and highlights potential sell opportunities. Liquidity points in this setup mark areas where Smart Money enters sell positions.
In the bearish setup image, a Lower High is followed by a Lower Low, with the red liquidity zone acting as a Bearish Order Block. The Smart Money Setup indicator shows a red arrow, signaling a likely downward move, offering traders an opportunity to enter short positions.
🔵 Settings
Pivot Period : This setting determines how many candles are needed to form a pivot point. A default value of 2 is optimal for quickly identifying key pivot points in price action.
Order Block Validity Period : This parameter defines the lifespan of an order block. Traders can adjust how long each order block remains valid. For instance, setting it to 500 means that an order block will be valid for 500 bars after its formation.
Mitigation Level OB : This setting allows traders to select whether order blocks should be based on the "Proximal," "50% OB," or "Distal" levels, helping traders manage risk more effectively.
Order Block Refinement : Traders can refine the order blocks with precision. The indicator offers two refinement modes: Defensive and Aggressive. The Defensive mode identifies safer order blocks, while the Aggressive mode targets higher-risk blocks with the potential for larger reversals.
🔵 Conclusion
The Smart Money Setup 07 indicator is a powerful tool for identifying key Smart Money movements in the market. It provides traders with essential insights for making informed trading decisions, particularly when combined with technical analysis and liquidity flow analysis. This indicator allows traders to accurately pinpoint entry and exit points, helping them maximize profits and minimize risk.
By offering a range of customizable settings, the Smart Money Setup indicator adapts to different trading styles and strategies. Furthermore, its ability to detect order blocks and identify supply and demand zones makes it an indispensable tool for any trader looking to enhance their strategy.
In conclusion, the Smart Money Setup 07 is a crucial tool for traders aiming to optimize their trading performance. By utilizing the concepts of Smart Money in technical analysis, traders can make more precise decisions and take advantage of market fluctuations.
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
Statistical ArbitrageThe Statistical Arbitrage Strategy, also known as pairs trading, is a quantitative trading method that capitalizes on price discrepancies between two correlated assets. The strategy assumes that over time, the prices of these two assets will revert to their historical relationship. The core idea is to take advantage of mean reversion, a principle suggesting that asset prices will revert to their long-term average after deviating significantly.
Strategy Mechanics:
1. Selection of Correlated Assets:
• The strategy focuses on two historically correlated assets (e.g., equity index futures like Dow Jones Mini and S&P 500 Mini). These assets tend to move in the same direction due to similar underlying fundamentals, such as overall market conditions. By tracking their relative prices, the strategy seeks to exploit temporary mispricings.
2. Spread Calculation:
• The spread is the difference between the prices of the two assets. This spread represents the relationship between the assets and serves as the basis for determining when to enter or exit trades.
3. Mean and Standard Deviation:
• The historical average (mean) of the spread is calculated using a Simple Moving Average (SMA) over a chosen period. The strategy also computes the standard deviation (volatility) of the spread, which measures how far the spread has deviated from the mean over time. This allows the strategy to define statistically significant price deviations.
4. Entry Signal (Mean Reversion):
• A buy signal is triggered when the spread falls below the mean by a multiple (e.g., two) of the standard deviation. This indicates that one asset is temporarily undervalued relative to the other, and the strategy expects the spread to revert to its mean, generating profits as the prices converge.
5. Exit Signal:
• The strategy exits the trade when the spread reverts to the mean. At this point, the mispricing has been corrected, and the profit from the mean reversion is realized.
Academic Support:
Statistical arbitrage has been widely studied in finance and economics. Gatev, Goetzmann, and Rouwenhorst’s (2006) landmark study on pairs trading demonstrated that this strategy could generate excess returns in equity markets. Their research found that by focusing on historically correlated stocks, traders could identify pricing anomalies and profit from their eventual correction.
Additionally, Avellaneda and Lee (2010) explored statistical arbitrage in different asset classes and found that exploiting deviations in price relationships can offer a robust, market-neutral trading strategy. In these studies, the strategy’s success hinges on the stability of the relationship between the assets and the timely execution of trades when deviations occur.
Risks of Statistical Arbitrage:
1. Correlation Breakdown:
• One of the primary risks is the breakdown of correlation between the two assets. Statistical arbitrage assumes that the historical relationship between the assets will hold in the future. However, market conditions, company fundamentals, or external shocks (e.g., macroeconomic changes) can cause these assets to deviate permanently, leading to potential losses.
• For instance, if two equity indices historically move together but experience divergent economic conditions or policy changes, their prices may no longer revert to the expected mean.
2. Execution Risk:
• This strategy relies on efficient execution and tight spreads. In volatile or illiquid markets, the actual price at which trades are executed may differ significantly from expected prices, leading to slippage and reduced profits.
3. Market Risk:
• Although statistical arbitrage is designed to be market-neutral (i.e., not dependent on the overall market direction), it is not entirely risk-free. Systematic market shocks, such as financial crises or sudden shifts in market sentiment, can affect both assets simultaneously, causing the spread to widen rather than revert to the mean.
4. Model Risk:
• The assumptions underlying the strategy, particularly regarding mean reversion, may not always hold true. The model assumes that asset prices will return to their historical averages within a certain timeframe, but the timing and magnitude of mean reversion can be uncertain. Misestimating this timeframe can lead to extended drawdowns or unrealized losses.
5. Overfitting:
• Over-reliance on historical data to fine-tune the strategy parameters (e.g., the lookback period or standard deviation thresholds) may result in overfitting. This means that the strategy works well on past data but fails to perform in live markets due to changing conditions.
Conclusion:
The Statistical Arbitrage Strategy offers a systematic and quantitative approach to trading that capitalizes on temporary price inefficiencies between correlated assets. It has been proven to generate returns in academic studies and is widely used by hedge funds and institutional traders for its market-neutral characteristics. However, traders must be aware of the inherent risks, including correlation breakdown, execution risks, and the potential for prolonged deviations from the mean. Effective risk management, diversification, and constant monitoring are essential for successfully implementing this strategy in live markets.
BTC ETF Flow Trading SignalsTracks large money flows (500M+) across major Bitcoin ETFs (IBIT, BTCO, FBTC, ARKB, BITB)
Generates long/short signals based on institutional money movement
Shows flow trends and strength of movements
This script provides a foundation for comparing ETF inflows and Bitcoin price. The effectiveness of the analysis depends on the quality of the data and your interpretation of the results. Key levels of 500M and 350M Inflow/Outflow Enjoy
Collaboration with Vivid Vibrations
Enjoy & improve!
Saturn Retrograde PeriodsSaturn Retrograde Periods Visualizer for TradingView
This Pine Script visualizes all Saturn retrograde periods since 2009, including the current retrograde ending on November 15, 2024. The script overlays yellow boxes on your TradingView chart to highlight the exact periods of Saturn retrograde. It's a great tool for astrologically-inclined traders or those interested in market timing based on astrological events.
Key Features:
Full Historical Coverage: Displays Saturn retrograde periods from 2009 (the inception of Bitcoin) to the current retrograde ending in November 2024.
Customizable Appearance: You can easily adjust the color and opacity of the boxes directly from the script's settings window, making it flexible for various chart styles.
Visual Clarity: The boxes span the full vertical range of your chart, ensuring the retrograde periods are clearly visible over any asset, timeframe, or price action.
How to Use:
Add the script to your TradingView chart.
Adjust the color and opacity in the settings to suit your preferences.
View all relevant Saturn retrograde periods and analyze how these astrological events may align with price movements in your selected asset.
This script is perfect for traders and analysts who want to combine astrology with financial market analysis!
scripted by chat.gpt - version 1.0
Macros ICT KillZones [TradingFinder] Times & Price Trading Setup🔵 Introduction
ICT Macros, developed by Michael Huddleston, also known as ICT (Inner Circle Trader), is a powerful trading tool designed to help traders identify the best trading opportunities during key time intervals like the London and New York trading sessions.
For traders aiming to capitalize on market volatility, liquidity shifts, and Fair Value Gaps (FVG), understanding and using these critical time zones can significantly improve trading outcomes.
In today’s highly competitive financial markets, identifying the moments when the market is seeking buy-side or sell-side liquidity, or filling price imbalances, is essential for maximizing profitability.
The ICT Macros indicator is built on the renowned ICT time and price theory, which enables traders to track and leverage key market dynamics such as breaks of highs and lows, imbalances, and liquidity hunts.
This indicator automatically detects crucial market times and optimizes strategies for traders by highlighting the specific moments when price movements are most likely to occur. A standout feature of ICT Macros is its automatic adjustment for Daylight Saving Time (DST), ensuring that traders remain synced with the correct session times.
This means you can rely on accurate market timing without the need for manual updates, allowing you to focus on capturing profitable trades during critical timeframes.
🔵 How to Use
The ICT Macros indicator helps you capitalize on trading opportunities during key market moments, particularly when the market is breaking highs or lows, filling Fair Value Gaps (FVG), or addressing imbalances. This indicator is particularly beneficial for traders who seek to identify liquidity, market volatility, and price imbalances.
🟣 Sessions
London Sessions
London Macro 1 :
UTC Time : 06:33 to 07:00
New York Time : 02:33 to 03:00
London Macro 2 :
UTC Time : 08:03 to 08:30
New York Time : 04:03 to 04:30
New York Sessions
New York Macro AM 1 :
UTC Time : 12:50 to 13:10
New York Time : 08:50 to 09:10
New York Macro AM 2 :
UTC Time : 13:50 to 14:10
New York Time : 09:50 to 10:10
New York Macro AM 3 :
UTC Time : 14:50 to 15:10
New York Time : 10:50 to 11:10
New York Lunch Macro :
UTC Time : 15:50 to 16:10
New York Time : 11:50 to 12:10
New York PM Macro :
UTC Time : 17:10 to 17:40
New York Time : 13:10 to 13:40
New York Last Hour Macro :
UTC Time : 19:15 to 19:45
New York Time : 15:15 to 15:45
These time intervals adjust automatically based on Daylight Saving Time (DST), helping traders to enter or exit trades during key market moments when price volatility is high.
Below are the main applications of this tool and how to incorporate it into your trading strategies :
🟣 Combining ICT Macros with Trading Strategies
The ICT Macros indicator can easily be used in conjunction with various trading strategies. Two well-known strategies that can be combined with this indicator include:
ICT 2022 Trading Model : This model is designed based on identifying market liquidity, structural price changes, and Fair Value Gaps (FVG). By using ICT Macros, you can identify the key time intervals when the market is seeking liquidity, filling imbalances, or breaking through important highs and lows, allowing you to enter or exit trades at the right moment.
Silver Bullet Strategy : This strategy, which is built around liquidity hunting and rapid price movements, can work more accurately with the help of ICT Macros. The indicator pinpoints precise liquidity times, helping traders take advantage of market shifts caused by filling Fair Value Gaps or correcting imbalances.
🟣 Capitalizing on Price Volatility During Key Times
Large market algorithms often seek liquidity or fill Fair Value Gaps (FVG) during the intervals marked by ICT Macros. These periods are when price volatility increases, and traders can use these moments to enter or exit trades.
For example, if sell-side liquidity is drained and the market fills an imbalance, the price might move toward buy-side liquidity. By identifying these moments, which may also involve breaking a previous high or low, you can leverage rapid market fluctuations to your advantage.
🟣 Identifying Liquidity and Price Imbalances
One of the important uses of ICT Macros is identifying points where the market is seeking liquidity and correcting imbalances. You can determine high or low liquidity levels in the market before each ICT Macro, as well as Fair Value Gaps (FVG) and price imbalances that need to be filled, using them to adjust your trading strategy. This capability allows you to manage trades based on liquidity shifts or imbalance corrections without needing a bias toward a specific direction.
🔵 Settings
The ICT Macros indicator offers various customization options, allowing users to tailor it to their specific needs. Below are the main settings:
Time Zone Mode : You can select one of the following options to define how time is displayed:
UTC : For traders who need to work with Universal Time.
Session Local Time : The local time corresponding to the London or New York markets.
Your Time Zone : You can specify your own time zone (e.g., "UTC-4:00").
Your Time Zone : If you choose "Your Time Zone," you can set your specific time zone. By default, this is set to UTC-4:00.
Show Range Time : This option allows you to display the time range of each session on the chart. If enabled, the exact start and end times of each interval are shown.
Show or Hide Time Ranges : Toggle on/off for visual clarity depending on user preference.
Custom Colors : Set distinct colors for each session, allowing users to personalize their chart based on their trading style.These settings allow you to adjust the key time intervals of each trading session to your preference and customize the time format according to your own needs.
🔵 Conclusion
The ICT Macros indicator is a powerful tool for traders, helping them to identify key time intervals where the market seeks liquidity or fills Fair Value Gaps (FVG), corrects imbalances, and breaks highs or lows. This tool is especially valuable for traders using liquidity-based strategies such as ICT 2022 or Silver Bullet.
One of the key features of this indicator is its support for Daylight Saving Time (DST), ensuring you are always in sync with the correct trading session timings without manual adjustments. This is particularly beneficial for traders operating across different time zones.
With ICT Macros, you can capitalize on crucial market opportunities during sensitive times, take advantage of imbalances, and enhance your trading strategies based on market volatility, liquidity shifts, and Fair Value Gaps.
BOS TRADER [v 1.0] [Influxum]The name of the tool, BOS Trader, comes from the abbreviation BOS, which stands for Break Of Structure. In simple terms, this tool identifies situations where a change in market structure occurs after liquidity has been grabbed. Following the structural change, it looks for a point where the balance between buyers and sellers will be tested, potentially continuing the price movement in the direction of the structural break.
The goal of this tool is to identify areas where a trader can look for potential entry opportunities based on their entry rules and filters. In our own research, we found that while this tool is not a standalone strategy, it provides a statistical advantage that stems from the nature of the market itself. If you expect the market to reverse at a certain price level against a short-term, medium-term, or long-term trend, that reversal must logically begin with a change in structure – i.e., its break. BOS Trader then highlights the zone where you can expect a strong reaction from traders speculating on the continuation of price in the direction of the break.
Another important piece of the puzzle is the concept of liquidity. Liquidity grabs are generally considered by traders to be events that can trigger market direction changes. That's why BOS Trader is complemented with multiple ways to identify liquidity in the market from a Price Action perspective. We have explored the liquidity concept in depth in our other tools – the Liquidity Tool and Liquidity Strategy Tester – so we won’t go into too much detail on liquidity settings here.
🟪 Pivots
Liquidity can be found beyond pivot extremes – the highest candles in a series of candles. The pivot liquidity setting specifies how many candles must be before and after the pivot candle with a lower high for a pivot high or a higher low for a pivot low. A pivot high is the local highest point of the last 31 candles (15 before the pivot candle, the pivot candle itself, and 15 after). Another option is to set the time period in which the pivot extreme must occur. For example, you can differentiate between pivot highs of the Asian or London session.
🟪 % Percent Change
This setting is based on the well-known Zig Zag indicator and confirms swing highs or swing lows when there is a certain percentage change in price. This helps filter out noise that can occur when the market consolidates and randomly creates pivot highs or lows that aren’t significant.
🟪 Session High/Low
Many popular strategies are based on liquidity defined as the price range of a specific trading session. This doesn't have to be London, Asia, or New York sessions, but could be, for instance, the first hour of the New York session, and so on.
🟪 Day High/Low, Week High/Low, Month High/Low
As the name suggests, liquidity is often defined by the high/low of the previous day, week, or month. These price levels are watched by many market participants, and it's reasonable to expect reactions at these levels. That’s why we included this option in the BOS tool.
Tip for Traders
To avoid common issues with setting the correct session time, we have added the BG option to the tool – the ability to display a background for the configured trading session. This makes it easy to verify that your trading session is set correctly in relation to your time zone.
Delete grabbed liquidity
If a liquidity level is breached by price, it becomes invalid. For those who prefer to keep their charts clean and uncluttered, there is an option to delete grabbed liquidity. This way, only untraded, valid liquidity lines will be visible on the chart.
Bars after liquidity grab
A liquidity grab should be a significant event that triggers a reaction from market participants. To ensure this is a real response to liquidity rather than random market behavior, we added a time test to the BOS tool. A structural break must occur within a specified time after the liquidity grab. You can define this time in the tool as the number of bars after which the structural break is still considered valid following the liquidity grab.
🟪 AOI (Area of Interest) Settings
Initially, it's important to note that there are two main options for setting the behavior of the AOI. The first option is to fix its duration by the number of bars – Duration, and the second is to keep the AOI valid until it is traded through – Extended.
Duration
Since we expect a quick reaction to the liquidity grab, we also expect a fast pullback to the AOI and a swift response of traders. Our research has shown that the strongest reactions typically occur within a maximum of 15 bars from the formation of the AOI (fractally across timeframes). Therefore, this value is set as the default. However, we recommend considering not just the speed of the reaction but also its intensity. After the set number of bars, the AOI stops extending further.
Extended
We have noticed that price has a tendency to return to the AOI even after a longer period and react again. For this reason, we included the option in the BOS tool to extend the AOI into the future, with the ability to freely adjust the Max AOI Length.
🟪 AOI Size Mode
There are two options for setting the size of the AOI. Either it can be calculated as a percentage of the swing size (% of swing) in which the structural break occurred (the default setting is 30%), or you can set a different concept for the AOI size. For example, the well-known Optimal Trade Entry model. Custom values can be set in the FIBO Levels option, where you can define either preferred Fibonacci values or values based on your own criteria.
🟪 Trading Session (signals + alerts + visibility)
The main goal of our tools is to make it easier for traders to identify patterns and opportunities in the market and allow them to be alerted to their occurrence. The time for AOI plotting after a liquidity grab is combined into a single Trading Session function. This controls both the AOI plotting and when the tool will send alerts. All of this is aimed at helping traders avoid spending the entire day in front of their monitors, waiting for trading opportunities. Here, too, you can use the BG feature to plot a background on the chart showing the current session.
🟪 Trading within session range
We found that some traders have difficulty navigating the many AOIs plotted during times when the market consolidates and creates numerous false breakouts. Therefore, we included an option in the BOS tool to track only structural changes at the price extremes of the current day and trading session. The tool will not plot structural changes for internal liquidity grabs (within the session range), but only for external liquidity grabs (highest highs and lowest lows of the session or liquidity from previous days).
Visuals
The BOS tool is, of course, supplemented with the option to customize the appearance of all its components according to your preferences.
Enhanced Kelly Criterion with Risk ManagementThis script is a trading tool for risk management and position size calculations based on the Kelly criteria. The objective is to calculate the optimal position size for each trade based on win/loss ratio and win/loss ratio to manage your money.
Overview
Initial Funding: Starting with an initial capital of $10,000, the balance (amount of funds) of both “bullish” and “bearish” positions will increase or decrease depending on the outcome of the trade.
Risk Management: Users can set their risk tolerance from 1-100%. In addition, the maximum position size per trade is also limited at 50%, for example. This setting allows the user to limit risk.
Record of trade results: For each trade, a positive (bullish) or negative (bearish) line is determined, and wins and losses are recorded accordingly. Win/loss ratios and win/loss ratios are also calculated in real time from this data.
Win rate: Calculates the percentage of winning trades in a trade.
Win/Loss Ratio: Calculates the ratio of profit/loss between positive and negative trades.
Position sizing using the Kelly Criterion: Based on the win/loss ratio, the optimal position size to take on the next trade is calculated using the Kelly Criterion. However, this Kelly Criterion is treated with caution because of the potential for increased risk.
Controlling Risk and Position Size
Volatility adjustment using ATR (Average True Range): The script considers market volatility (range of price fluctuation) using a measure called ATR. This allows for smaller position sizes when price volatility is high, thereby reducing risk.
Position Size Limit: The maximum position size is limited so that the calculated position size does not exceed a certain range. This reduces the risk of large losses.
Display of Results
The script visually plots the final position size and amount of funds so that traders can see the changes in balance. To highlight points of change, position size expansions and contractions are shown, allowing traders to catch signs of sudden fluctuations or changes in volatility.
Suggested Improvements and Considerations
Kelly Criteria Overexposure Risk: Calculations based on the Kelly Criteria are theoretically correct, but they tend to take large positions. This can be very damaging in the event of losses. Therefore, while this script limits risk by setting a maximum position size, it is recommended that you adjust to an even more modest position size.
Data Reliability: The calculation of win/loss ratios and win/loss ratios relies on historical trade data, which can be unreliable until sufficient trade data is gathered. When trade data is scarce, calculations based on the Kelly Criteria may be overly optimistic.
Volatility considerations: Volatility adjustment using ATR is effective, but ATR alone may not be sufficient when markets fluctuate rapidly; if ATR adjustment is insufficient, additional risk mitigation techniques should be used in conjunction.
Overall, this script emphasizes risk management and optimizes position size using the Kelly criteria, but real market conditions require careful risk management with attention to overexposure.
NYSE, Euronext, and Shanghai Stock Exchange Hours IndicatorNYSE, Euronext, and Shanghai Stock Exchange Hours Indicator
This script is designed to enhance your trading experience by visually marking the opening and closing hours of major global stock exchanges: the New York Stock Exchange (NYSE), Euronext, and Shanghai Stock Exchange. By adding vertical lines and background fills during trading sessions, it helps traders quickly identify these critical periods, potentially informing better trading decisions.
Features of This Indicator:
NYSE, Euronext, and Shanghai Stock Exchange Hours: Displays vertical lines at market open and close times for these three exchanges. You can easily switch between showing or hiding the different exchanges to customize the indicator for your needs.
Background Fill: Highlights the trading hours of these exchanges using faint background colors, making it easy to spot when markets are in session. This feature is crucial for timing trades around overlapping trading hours and volume peaks.
Customizable Visuals: Adjust the color, line style (solid, dotted, dashed), and line width to match your preferences, making the indicator both functional and visually aligned with your chart's aesthetics.
How to Use the Indicator:
Add the Indicator to Your Chart: Add the script to your chart from the TradingView script library. Once added, the indicator will automatically plot vertical lines at the opening and closing times of the NYSE, Euronext, and Shanghai Stock Exchange.
Customize Display Settings: Choose which exchanges to display by enabling or disabling the NYSE, Euronext, or Shanghai sessions in the indicator settings. This allows you to focus only on the exchanges that are relevant to your trading strategy.
Adjust Visual Properties: Customize the appearance of the vertical lines and background fill through the settings. Modify the color of each exchange, adjust the line style (solid, dotted, dashed), and control the line thickness to suit your chart preferences. The background fill can also be customized to clearly highlight active trading sessions.
Identify Key Market Hours: Use the vertical lines and background fills to identify the market open and close times. This is particularly useful for understanding how price action changes during specific trading hours or for finding high liquidity periods when multiple markets are open simultaneously.
Adapt Trading Strategies: By knowing when major stock exchanges are open, you can adapt your trading strategy to take advantage of potential price movements, increased volatility, or volume. This can help you avoid low-liquidity times and capitalize on more active trading periods.
This indicator is especially valuable for traders focusing on cross-market dynamics or those interested in understanding how different sessions influence market liquidity and price action. With this tool, you can gain insight into market conditions and adapt your trading strategies accordingly. The clean visual separation of session times helps you maintain context, whether you're trading Forex, stocks, or cryptocurrencies.
Disclaimer: This script is intended for informational and educational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument. Always conduct your own research and consult with a licensed financial advisor before making any trading decisions. Trading involves risk, and past performance is not indicative of future results.
DYNAMIC USD MOMENTUM INDICATOR
Hello traders,
Welcome to my script, an indicator helping you to quickly see the performance of USD in constant daily comparison to other currencies.
This script requests price data from other charts but displays overbought and oversold labels on any selected chart currency pair.
See attached images to spot high probability reversal days when USD is in extremes against multiple other currencies. The output labels represent the currency traded against USD and reaching overbought and oversold zoned on a dynamic RSI scale.
Suggested pairs with higher co relation to stronger or weaker dollar:
AUD/USD, CAD/USD, EUR/USD, GBP/USD, NZD/USD
CHF/USD and JPY/USD require more in depth analysis of individual performance of JPY AND CHF
Zone Color PatternZone Color Pattern indicator depicts the color pattern of zones on chart. This will help the user to identify the zones on Chart.
Green Zone is indicated by Green color.
Red Zone is indicated by Red Color.
Gray Zone is indicated by Gray Zone.
Zone Color Pattern indicator is based on 3 moving averages. Long term, Medium term and Short Term.By default they are 200, 50 and 20.
When you are on long term trend the position of MAs is 20 MA is on top,then comes 50 MA and 200 MA is positioned below 50 MA.The position of respective MAs change during down trend.
The color patterns display the distance between different MAs .The widening and contraction of space between different Moving Averages indicate the movement and direction of price.
Basically price tend to move in and move away from Average. This action tend to create a space between price and MAs.Color patterns between price and MAs reflect the gap between the price and M|As .All these effects can be visualized on chart in relevant colors to infer the status of price, movement, cross over by the User.
Buy trades are preferred when close is in Green Zone and price is above MA20.
Sell trades are preferred when close is in Red Zone and price is below MA20
Trades may be avoided when close is in Gray Zone.
Long Up Trend and Down Trend respective color triangle shapes and arrows on chart indicate the trends and direction.
The chart understanding has to be supplemented with other regular indicators along with appropriate risk reward techniques by user.
Table indicate difference between Last Price traded and Day open price.
Other columns in table display the position of close in different Zones.
DISCLAIMER: For educational and entertainment purpose only .Nothing in this content should be interpreted as financial advice or a recommendation to buy or sell any sort of security/ies or investment/s.
RSI Weighted Trend System I [InvestorUnknown]The RSI Weighted Trend System I is an experimental indicator designed to combine both slow-moving trend indicators for stable trend identification and fast-moving indicators to capture potential major turning points in the market. The novelty of this system lies in the dynamic weighting mechanism, where fast indicators receive weight based on the current Relative Strength Index (RSI) value, thus providing a flexible tool for traders seeking to adapt their strategies to varying market conditions.
Dynamic RSI-Based Weighting System
The core of the indicator is the dynamic weighting of fast indicators based on the value of the RSI. In essence, the higher the absolute value of the RSI (whether positive or negative), the higher the weight assigned to the fast indicators. This enables the system to capture rapid price movements around potential turning points.
Users can choose between a threshold-based or continuous weight system:
Threshold-Based Weighting: Fast indicators are activated only when the absolute RSI value exceeds a user-defined threshold. Below this threshold, fast indicators receive no weight.
Continuous Weighting: By setting the weight threshold to zero, the fast indicators always receive some weight, although this can result in more false signals in ranging markets.
// Calculate weight for Fast Indicators based on RSI (Slow Indicator weight is kept to 1 for simplicity)
f_RSI_Weight_System(series float rsi, simple float weight_thre) =>
float fast_weight = na
float slow_weight = na
if weight_thre > 0
if math.abs(rsi) <= weight_thre
fast_weight := 0
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
else
fast_weight := 0 + math.sqrt(math.abs(rsi))
slow_weight := 1
Slow and Fast Indicators
Slow Indicators are designed to identify stable trends, remaining constant in weight. These include:
DMI (Directional Movement Index) For Loop
CCI (Commodity Channel Index) For Loop
Aroon For Loop
Fast Indicators are more responsive and designed to spot rapid trend shifts:
ZLEMA (Zero-Lag Exponential Moving Average) For Loop
IIRF (Infinite Impulse Response Filter) For Loop
Each of these indicators is calculated using a for-loop method to generate a moving average, which captures the trend of a given length range.
RSI Normalization
To facilitate the weighting system, the RSI is normalized from its usual 0-100 range to a -1 to 1 range. This allows for easy scaling when calculating weights and helps the system adjust to rapidly changing market conditions.
// Normalize RSI (1 to -1)
f_RSI(series float rsi_src, simple int rsi_len, simple string rsi_wb, simple string ma_type, simple int ma_len) =>
output = switch rsi_wb
"RAW RSI" => ta.rsi(rsi_src, rsi_len)
"RSI MA" => ma_type == "EMA" ? (ta.ema(ta.rsi(rsi_src, rsi_len), ma_len)) : (ta.sma(ta.rsi(rsi_src, rsi_len), ma_len))
Signal Calculation
The final trading signal is a weighted average of both the slow and fast indicators, depending on the calculated weights from the RSI. This ensures a balanced approach, where slow indicators maintain overall trend guidance, while fast indicators provide timely entries and exits.
// Calculate Signal (as weighted average)
sig = math.round(((DMI*slow_w) + (CCI*slow_w) + (Aroon*slow_w) + (ZLEMA*fast_w) + (IIRF*fast_w)) / (3*slow_w + 2*fast_w), 2)
Backtest Mode and Performance Metrics
This version of the RSI Weighted Trend System includes a comprehensive backtesting mode, allowing users to evaluate the performance of their selected settings against a Buy & Hold strategy. The backtesting includes:
Equity calculation based on the signals generated by the indicator.
Performance metrics table comparing Buy & Hold strategy metrics with the system’s signals, including: Mean, positive, and negative return percentages, Standard deviations (of all, positive and negative returns), Sharpe Ratio, Sortino Ratio, and Omega Ratio
f_PerformanceMetrics(series float base, int Lookback, simple float startDate, bool Annualize = true) =>
// Initialize variables for positive and negative returns
pos_sum = 0.0
neg_sum = 0.0
pos_count = 0
neg_count = 0
returns_sum = 0.0
returns_squared_sum = 0.0
pos_returns_squared_sum = 0.0
neg_returns_squared_sum = 0.0
// Loop through the past 'Lookback' bars to calculate sums and counts
if (time >= startDate)
for i = 0 to Lookback - 1
r = (base - base ) / base
returns_sum += r
returns_squared_sum += r * r
if r > 0
pos_sum += r
pos_count += 1
pos_returns_squared_sum += r * r
if r < 0
neg_sum += r
neg_count += 1
neg_returns_squared_sum += r * r
float export_array = array.new_float(12)
// Calculate means
mean_all = math.round((returns_sum / Lookback) * 100, 2)
mean_pos = math.round((pos_count != 0 ? pos_sum / pos_count : na) * 100, 2)
mean_neg = math.round((neg_count != 0 ? neg_sum / neg_count : na) * 100, 2)
// Calculate standard deviations
stddev_all = math.round((math.sqrt((returns_squared_sum - (returns_sum * returns_sum) / Lookback) / Lookback)) * 100, 2)
stddev_pos = math.round((pos_count != 0 ? math.sqrt((pos_returns_squared_sum - (pos_sum * pos_sum) / pos_count) / pos_count) : na) * 100, 2)
stddev_neg = math.round((neg_count != 0 ? math.sqrt((neg_returns_squared_sum - (neg_sum * neg_sum) / neg_count) / neg_count) : na) * 100, 2)
// Calculate probabilities
prob_pos = math.round((pos_count / Lookback) * 100, 2)
prob_neg = math.round((neg_count / Lookback) * 100, 2)
prob_neu = math.round(((Lookback - pos_count - neg_count) / Lookback) * 100, 2)
// Calculate ratios
sharpe_ratio = math.round(mean_all / stddev_all * (Annualize ? math.sqrt(Lookback) : 1), 2)
sortino_ratio = math.round(mean_all / stddev_neg * (Annualize ? math.sqrt(Lookback) : 1), 2)
omega_ratio = math.round(pos_sum / math.abs(neg_sum), 2)
// Set values in the array
array.set(export_array, 0, mean_all), array.set(export_array, 1, mean_pos), array.set(export_array, 2, mean_neg),
array.set(export_array, 3, stddev_all), array.set(export_array, 4, stddev_pos), array.set(export_array, 5, stddev_neg),
array.set(export_array, 6, prob_pos), array.set(export_array, 7, prob_neu), array.set(export_array, 8, prob_neg),
array.set(export_array, 9, sharpe_ratio), array.set(export_array, 10, sortino_ratio), array.set(export_array, 11, omega_ratio)
// Export the array
export_array
The metrics help traders assess the effectiveness of their strategy over time and can be used to optimize their settings.
Calibration Mode
A calibration mode is included to assist users in tuning the indicator to their specific needs. In this mode, traders can focus on a specific indicator (e.g., DMI, CCI, Aroon, ZLEMA, IIRF, or RSI) and fine-tune it without interference from other signals.
The calibration plot visualizes the chosen indicator's performance against a zero line, making it easy to see how changes in the indicator’s settings affect its trend detection.
Customization and Default Settings
Important Note: The default settings provided are not optimized for any particular market or asset. They serve as a starting point for experimentation. Traders are encouraged to calibrate the system to suit their own trading strategies and preferences.
The indicator allows deep customization, from selecting which indicators to use, adjusting the lengths of each indicator, smoothing parameters, and the RSI weight system.
Alerts
Traders can set alerts for both long and short signals when the indicator flips, allowing for automated monitoring of potential trading opportunities.
Option Delta Candles [Luxmi AI]Introduction
In the world of options trading, understanding how an option’s price changes with various factors is crucial. One of the key metrics traders use is **Delta**, which measures the sensitivity of an option’s price to changes in the price of the underlying asset. This blog explores an Option Delta Indicator with an Exponential Moving Average (EMA), including its uses, how it works, and its potential limitations.
What is the Option Delta Indicator?
Delta is one of the "Greeks" used in options trading to gauge the risk and behavior of options. It indicates how much an option's price is expected to change for a one-point move in the underlying asset's price. Specifically:
- Call Option Delta: A positive value indicating that the option's price increases as the underlying price increases.
- Put Option Delta: A negative value indicating that the option's price decreases as the underlying price increases.
Key Features of the Indicator
Delta Calculation
The Option Delta Indicator calculates the delta of a call option using the Black-Scholes model, a widely accepted method for pricing European-style options. The formula for delta in the context of a call option is:
Delta = N(d1)
Where:
d1 is calculated as:
d1 = (ln(S / K) + (r + (σ^2 / 2)) * T) / (σ * sqrt(T))
Here, S is the current market price of the option (used as the strike price in this case), K is the strike price, r is the risk-free interest rate, σ is the volatility, and T is the time to expiry in years.
EMA of Delta
The Exponential Moving Average (EMA) of the delta is also plotted. The EMA is a smoothing function that helps identify trends by giving more weight to recent data points. It is calculated as:
EMA = ta.ema(delta_call, emaLength)
Where `emaLength` is the user-defined period for the EMA.
Uses of the Option Delta Indicator
Trend Analysis
The EMA helps smooth out delta values, making it easier to identify trends in the delta over time. This can be useful for traders looking to understand whether the delta is increasing or decreasing, which may indicate how the option’s sensitivity to price changes is evolving.
Decision-Making Tool
By observing both delta and its EMA, traders can make more informed decisions. For instance, if the delta is rising and the EMA confirms this trend, it might indicate bullish momentum in the underlying asset. Conversely, a declining delta with a falling EMA could suggest bearish trends.
Risk Management
Understanding the delta can help traders manage their risk by assessing how sensitive their options positions are to movements in the underlying asset. By using the EMA of delta, traders can better gauge changes in sensitivity and adjust their positions accordingly.
Limitations and Disadvantages
Dependence on Model Assumptions
The Black-Scholes model, which is used to calculate delta, relies on several assumptions including constant volatility and interest rates, and the absence of dividends. These assumptions may not hold in real-world markets, potentially affecting the accuracy of delta calculations.
No Consideration of Market Conditions
The indicator does not account for broader market conditions or liquidity factors. Delta and its EMA are calculated based purely on price and time to expiry, without incorporating market news or events that might impact the option's price.
Lag in EMA
The EMA, while smoothing data, introduces a lag because it is based on past prices. This means that the EMA may not react immediately to sudden price changes, potentially causing delayed signals.
Simplified Strike Price
In this indicator, the strike price is set to the current market price of the option. This simplification might not be suitable for all trading strategies, particularly if a different strike price is more relevant to the trader's strategy.
Limited Scope
This indicator focuses solely on delta and its EMA. While useful, it does not provide a comprehensive view of an option’s overall risk profile. Traders should consider using additional indicators and analyses for a more complete understanding.
Conclusion
The Option Delta Indicator with EMA offers a useful tool for traders to analyze how the sensitivity of an option’s price to changes in the underlying asset’s price evolves over time. The inclusion of an EMA helps to smooth out the delta values and identify trends. However, traders should be aware of the limitations, including the model’s assumptions, potential lag in EMA signals, and the simplified approach to the strike price.
As with any trading tool, it's crucial to use this indicator as part of a broader trading strategy that includes other analyses and risk management practices. Understanding its strengths and limitations will help traders make more informed decisions and enhance their overall trading effectiveness.
ICT Killzones with Market BehaviorIndicator Title: ICT Killzones with Market Behavior
Description:
Introducing the ICT Killzones with Market Behavior indicator, a powerful tool designed for traders looking to capitalize on key trading timeframes in the New York session.
Key Features:
Visual Timeframes: This indicator highlights essential trading periods, including Pre-Market, NY Open, NY Lunch, and NY PM sessions. These visual markers help you quickly identify when the market is likely to experience heightened activity and liquidity.
Real-Time Insights: Stay informed with dynamic text displayed at the top of your chart. The indicator updates in real-time, providing actionable insights on what to expect during each session—whether it’s volatility, consolidation, or potential trend continuation.
Custom Color Options: Tailor the color settings for each killzone to fit your personal trading style and enhance the visual clarity of your chart.
User-Friendly Design: Built with simplicity in mind, this indicator integrates seamlessly into TradingView, making it easy for traders of all experience levels to utilize.
How to Use:
Add to Your Chart: Integrate the ICT Killzones with Market Behavior indicator into your TradingView setup.
Monitor Market Conditions: Keep an eye on the highlighted timeframes and the real-time insights displayed at the top. This information can guide your trading strategy effectively.
Adjust Your Approach: Use the insights from the indicator to modify your trading decisions based on the expected market behavior during each session.
Note: This indicator is for educational purposes only and should not be interpreted as financial advice. Always perform your own research and assess risks before making any trading decisions.
Bullseye NYSE 1st5mThis script, "BullseyeNYSE1st5m," is a TradingView indicator designed to highlight the high and low price levels during the first 5 minutes of the NYSE trading session. It works as follows:
1. **Identify NYSE Trading Hours**: The script identifies bars that fall within NYSE trading hours, specifically focusing on the first five minutes after the market opens.
2. **Calculate First 5-Minute High and Low**: During the first five minutes of the trading day, the script captures and updates the high and low prices, storing these values for the remainder of the session.
3. **Plot High and Low Levels**: The high and low values from the first five minutes are plotted as lines on the chart in yellow. This helps traders quickly identify the initial range set by the market.
4. **Fill the Area Between High and Low**: The area between the high and low levels is filled with a translucent yellow color to visually emphasize the first five-minute range.
5. **Alerts for Breakouts**: Alerts are set to notify the user when the price closes above or below the first five-minute range. This helps traders stay informed of potential breakout opportunities beyond this key opening range.
This indicator is useful for day traders looking to leverage the first few minutes of NYSE trading to identify early support and resistance levels and to spot breakout opportunities.
