QuantBuilder | FractalystWhat's the strategy's purpose and functionality?
QuantBuilder is designed for both traders and investors who want to utilize mathematical techniques to develop profitable strategies through backtesting on historical data.
The primary goal is to develop profitable quantitive strategies that not only outperform the underlying asset in terms of returns but also minimize drawdown.
For instance, consider Bitcoin (BTC), which has experienced significant volatility, averaging an estimated 200% annual return over the past decade, with maximum drawdowns exceeding -80%. By employing this strategy with diverse entry and exit techniques, users can potentially seek to enhance their Compound Annual Growth Rate (CAGR) while managing risk to maintain a lower maximum drawdown.
While this strategy employs quantitative techniques, including mathematical methods such as probabilities and positive expected values, it demonstrates exceptional efficacy across all markets. It particularly excels in futures, indices, stocks, cryptocurrencies, and commodities, leveraging their inherent trending behaviors for optimized performance.
In both trending and consolidating market conditions, QuantBuilder employs a combination of multi-timeframe probabilities, expected values, directional biases, moving averages and diverse entry models to identify and capitalize on bullish market movements.
How does the strategy perform for both investors and traders?
The strategy has two main modes, tailored for different market participants: Traders and Investors.
1. Trading:
- Designed for traders looking to capitalize on bullish markets.
- Utilizes a percentage risk per trade to manage risk and optimize returns.
- Suitable for both swing and intraday trading with a focus on probabilities and risk per trade approach.
2. Investing:
- Geared towards investors who aim to capitalize on bullish trending markets without using leverage while mitigating the asset's maximum drawdown.
- Utilizes pre-define percentage of the equity to buy, hold, and manage the asset.
- Focuses on long-term growth and capital appreciation by fully/partially investing in the asset during bullish conditions.
How does the strategy identify market structure? What are the underlying calculations?
The strategy utilizes an efficient logic with for loops to pinpoint the first swing candle featuring a pivot of 2, establishing the point at which the break of structure begins.
What entry criteria are used in this script? What are the underlying calculations?
The script utilizes two entry models: BreakOut and fractal.
Underlying Calculations:
Breakout: The script assigns the most recent swing high to a variable. When the price closes above this level and all other conditions are met, the script executes a breakout entry (conservative approach).
Fractal: The script identifies a swing low with a period of 2. Once this condition is met, the script executes the trade (aggressive approach).
How does the script calculate probabilities? What are the underlying calculations?
The script calculates probabilities by monitoring price interactions with liquidity levels. Here’s how the underlying calculations work:
Tracking Price Hits: The script counts the number of times the price taps into each liquidity side after the EQM level is activated. This data is stored in an array for further analysis.
Sample Size Consideration: The total number of price interactions serves as the sample size for calculating probabilities.
Probability Calculation: For each liquidity side, the script calculates the probability by taking the average of the recorded hits. This allows for a dynamic assessment of the likelihood that a particular side will be hit next, based on historical performance.
Dynamic Adjustment: As new price data comes in, the probabilities are recalculated, providing real-time aduptive insights into market behavior.
Note: The calculations are performed independently for each directional range. A range is considered bearish if the previous breakout was through a sellside liquidity. Conversely, a range is considered bullish if the most recent breakout was through a buyside liquidity.
How does the script calculate expected values? What are the underlying calculations?
The script calculates expected values by leveraging the probabilities of winning and losing trades, along with their respective returns. The process involves the following steps:
This quantitative methodology provides a robust framework for assessing the expected performance of trading strategies based on historical data and backtesting results.
How is the contextual bias calculated? What are the underlying calculations?
The contextual bias in the QuantBuilder script is calculated through a structured approach that assesses market structure based on swing highs and lows. Here’s how it works:
Identification of Swing Points: The script identifies significant swing points using a defined pivot logic, focusing on the first swing high and swing low. This helps establish critical levels for determining market structure.
Break of Structure (BOS) Assessment:
Bullish BOS: The script recognizes a bullish break of structure when a candle closes above the first swing high, followed by at least one swing low.
Bearish BOS: Conversely, a bearish break of structure is identified when a candle closes below the first swing low, followed by at least one swing high.
Bias Assignment: Based on the identified break of structure, the script assigns directional biases:
A bullish bias is assigned if a bullish BOS is confirmed.
A bearish bias is assigned if a bearish BOS is confirmed.
Quantitative Evaluation: Each identified bias is quantitatively evaluated, allowing the script to assign numerical values representing the strength of each bias. This quantification aids in assessing the reliability of market sentiment across multiple timeframes.
What's the purpose of using moving averages in this strategy? What are the underlying calculations?
Using moving averages is a widely-used technique to trade with the trend.
The main purpose of using moving averages in this strategy is to filter out bearish price action and to only take trades when the price is trading ABOVE specified moving averages.
The script uses different types of moving averages with user-adjustable timeframes and periods/lengths, allowing traders to try out different variations to maximize strategy performance and minimize drawdowns.
By applying these calculations, the strategy effectively identifies bullish trends and avoids market conditions that are not conducive to profitable trades.
The MA filter allows traders to choose whether they want a specific moving average above or below another one as their entry condition.
What type of stop-loss identification method are used in this strategy? What are the underlying calculations?
- Initial Stop-loss:
1. ATR Based:
The Average True Range (ATR) is a method used in technical analysis to measure volatility. It is not used to indicate the direction of price but to measure volatility, especially volatility caused by price gaps or limit moves.
Calculation:
- To calculate the ATR, the True Range (TR) first needs to be identified. The TR takes into account the most current period high/low range as well as the previous period close.
The True Range is the largest of the following:
- Current Period High minus Current Period Low
- Absolute Value of Current Period High minus Previous Period Close
- Absolute Value of Current Period Low minus Previous Period Close
- The ATR is then calculated as the moving average of the TR over a specified period. (The default period is 14)
2. ADR Based:
The Average Day Range (ADR) is an indicator that measures the volatility of an asset by showing the average movement of the price between the high and the low over the last several days.
Calculation:
- To calculate the ADR for a particular day:
- Calculate the average of the high prices over a specified number of days.
- Calculate the average of the low prices over the same number of days.
- Find the difference between these average values.
- The default period for calculating the ADR is 14 days. A shorter period may introduce more noise, while a longer period may be slower to react to new market movements.
3. PL Based:
This method places the stop-loss at the low of the previous candle.
If the current entry is based on the hunt entry strategy, the stop-loss will be placed at the low of the candle that wicks through the lower FRMA band.
Example:
If the previous candle's low is 100, then the stop-loss will be set at 100.
This method ensures the stop-loss is placed just below the most recent significant low, providing a logical and immediate level for risk management.
- Trailing Stop-Loss:
One of the key elements of this strategy is its ability to detect structural liquidity and structural invalidation levels across multiple timeframes to trail the stop-loss once the trade is in running profits.
By utilizing this approach, the strategy allows enough room for price to run.
By using these methods, the strategy dynamically adjusts the initial stop-loss based on market volatility, helping to protect against adverse price movements while allowing for enough room for trades to develop.
Each market behaves differently across various timeframes, and it is essential to test different parameters and optimizations to find out which trailing stop-loss method gives you the desired results and performance.
What type of break-even and take profit identification methods are used in this strategy? What are the underlying calculations?
For Break-Even:
Percentage (%) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain percentage above the entry.
Calculation:
Break-even level = Entry Price * (1 + Percentage / 100)
Example:
If the entry price is $100 and the break-even percentage is 5%, the break-even level is $100 * 1.05 = $105.
Risk-to-Reward (RR) Based:
Moves the initial stop-loss to the entry price when the price reaches a certain RR ratio.
Calculation:
Break-even level = Entry Price + (Initial Risk * RR Ratio)
For TP1 (Take Profit 1):
- You can choose to set a take profit level at which your position gets fully closed or 50% if the TP2 boolean is enabled.
- Similar to break-even, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP1 level as a percentage amount above the entry price or based on RR.
For TP2 (Take Profit 2):
- You can choose to set a take profit level at which your position gets fully closed.
- As with break-even and TP1, you can select either a percentage (%) or risk-to-reward (RR) based take profit level, allowing you to set your TP2 level as a percentage amount above the entry price or based on RR.
What's the day filter Filter, what does it do?
The day filter allows users to customize the session time and choose the specific days they want to include in the strategy session. This helps traders tailor their strategies to particular trading sessions or days of the week when they believe the market conditions are more favorable for their trading style.
Customize Session Time:
Users can define the start and end times for the trading session.
This allows the strategy to only consider trades within the specified time window, focusing on periods of higher market activity or preferred trading hours.
Select Days:
Users can select which days of the week to include in the strategy.
This feature is useful for excluding days with historically lower volatility or unfavorable trading conditions (e.g., Mondays or Fridays).
Benefits:
Focus on Optimal Trading Periods:
By customizing session times and days, traders can focus on periods when the market is more likely to present profitable opportunities.
Avoid Unfavorable Conditions:
Excluding specific days or times can help avoid trading during periods of low liquidity or high unpredictability, such as major news events or holidays.
What tables are available in this script?
- Summary: Provides a general overview, displaying key performance parameters such as Net Profit, Profit Factor, Max Drawdown, Average Trade, Closed Trades and more.
Total Commission: Displays the cumulative commissions incurred from all trades executed within the selected backtesting window. This value is derived by summing the commission fees for each trade on your chart.
Average Commission: Represents the average commission per trade, calculated by dividing the Total Commission by the total number of closed trades. This metric is crucial for assessing the impact of trading costs on overall profitability.
Avg Trade: The sum of money gained or lost by the average trade generated by a strategy. Calculated by dividing the Net Profit by the overall number of closed trades. An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.
MaxDD: Displays the largest drawdown of losses, i.e., the maximum possible loss that the strategy could have incurred among all of the trades it has made. This value is calculated separately for every bar that the strategy spends with an open position.
Profit Factor: The amount of money a trading strategy made for every unit of money it lost (in the selected currency). This value is calculated by dividing gross profits by gross losses.
Avg RR: This is calculated by dividing the average winning trade by the average losing trade. This field is not a very meaningful value by itself because it does not take into account the ratio of the number of winning vs losing trades, and strategies can have different approaches to profitability. A strategy may trade at every possibility in order to capture many small profits, yet have an average losing trade greater than the average winning trade. The higher this value is, the better, but it should be considered together with the percentage of winning trades and the net profit.
Winrate: The percentage of winning trades generated by a strategy. Calculated by dividing the number of winning trades by the total number of closed trades generated by a strategy. Percent profitable is not a very reliable measure by itself. A strategy could have many small winning trades, making the percent profitable high with a small average winning trade, or a few big winning trades accounting for a low percent profitable and a big average winning trade. Most mean-reversion successful strategies have a percent profitability of 40-80% but are profitable due to risk management control.
BE Trades: Number of break-even trades, excluding commission/slippage.
Losing Trades: The total number of losing trades generated by the strategy.
Winning Trades: The total number of winning trades generated by the strategy.
Total Trades: Total number of taken traders visible your charts.
Net Profit: The overall profit or loss (in the selected currency) achieved by the trading strategy in the test period. The value is the sum of all values from the Profit column (on the List of Trades tab), taking into account the sign.
- Monthly: Displays performance data on a month-by-month basis, allowing users to analyze performance trends over each month and year.
- Weekly: Displays performance data on a week-by-week basis, helping users to understand weekly performance variations.
- UI Table: A user-friendly table that allows users to view and save the selected strategy parameters from user inputs. This table enables easy access to key settings and configurations, providing a straightforward solution for saving strategy parameters by simply taking a screenshot with Alt + S or ⌥ + S.
User-input styles and customizations:
To facilitate studying historical data, all conditions and filters can be applied to your charts. By plotting background colors on your charts, you'll be able to identify what worked and what didn't in certain market conditions.
Please note that all background colors in the style are disabled by default to enhance visualization.
How to Use This Quantitive Strategy Builder to Create a Profitable Edge and System?
Choose Your Strategy mode:
- Decide whether you are creating an investing strategy or a trading strategy.
Select a Market:
- Choose a one-sided market such as stocks, indices, or cryptocurrencies.
Historical Data:
- Ensure the historical data covers at least 10 years of price action for robust backtesting.
Timeframe Selection:
- Choose the timeframe you are comfortable trading with. It is strongly recommended to use a timeframe above 15 minutes to minimize the impact of commissions/slippage on your profits.
Set Commission and Slippage:
- Properly set the commission and slippage in the strategy properties according to your broker/prop firm specifications.
Parameter Optimization:
- Use trial and error to test different parameters until you find the performance results you are looking for in the summary table or, preferably, through deep backtesting using the strategy tester.
Trade Count:
- Ensure the number of trades is 200 or more; the higher, the better for statistical significance.
Positive Average Trade:
- Make sure the average trade is above zero.
(An important value since it must be large enough to cover the commission and slippage costs of trading the strategy and still bring a profit.)
Performance Metrics:
- Look for a high profit factor, and net profit with minimum drawdown.
- Ideally, aim for a drawdown under 20-30%, depending on your risk tolerance.
Refinement and Optimization:
- Try out different markets and timeframes.
- Continue working on refining your edge using the available filters and components to further optimize your strategy.
What makes this strategy original?
QuantBuilder stands out due to its unique combination of quantitative techniques and innovative algorithms that leverage historical data for real-time trading decisions. Unlike most algorithmic strategies that work based on predefined rules, this strategy adapts to real-time market probabilities and expected values, enhancing its reliability. Key features include:
Mathematical Framework: The strategy integrates advanced mathematical concepts, such as probabilities and expected values, to assess trade viability and optimize decision-making.
Multi-Timeframe Analysis: By utilizing multi-timeframe probabilities, QuantBuilder provides a comprehensive view of market conditions, enhancing the accuracy of entry and exit points.
Dynamic Market Structure Identification: The script employs a systematic approach to identify market structure changes, utilizing a blend of swing highs and lows to detect contextual/direction bias of the market.
Built-in Trailing Stop Loss: The strategy features a dynamic trailing stop loss based on multi-timeframe analysis of market structure. This allows traders to lock in profits while adapting to changing market conditions, ensuring that exits are executed at optimal levels without prematurely closing positions.
Robust Performance Metrics: With detailed performance tables and visualizations, users can easily evaluate strategy effectiveness and adjust parameters based on historical performance.
Adaptability: The strategy is designed to work across various markets and timeframes, making it versatile for different trading styles and objectives.
Suitability for Investors and Traders: QuantBuilder is ideal for both investors and traders looking to rely on mathematically proven data to create profitable strategies, ensuring that decisions are grounded in quantitative analysis.
These original elements combine to create a powerful tool that can help both traders and investors to build and refine profitable strategies based on algorithmic quantitative analysis.
Terms and Conditions | Disclaimer
Our charting tools are provided for informational and educational purposes only and should not be construed as financial, investment, or trading advice. They are not intended to forecast market movements or offer specific recommendations. Users should understand that past performance does not guarantee future results and should not base financial decisions solely on historical data.
Built-in components, features, and functionalities of our charting tools are the intellectual property of @Fractalyst Unauthorized use, reproduction, or distribution of these proprietary elements is prohibited.
By continuing to use our charting tools, the user acknowledges and accepts the Terms and Conditions outlined in this legal disclaimer and agrees to respect our intellectual property rights and comply with all applicable laws and regulations.
Manajemen portofolio
Winning and Losing StreaksThe Pine Script indicator "Winning and Losing Streaks" tracks and visualizes the length of consecutive winning and losing streaks in a financial series, such as stock prices. Here’s a detailed description of the indicator, including the relevance of statistical analysis and streak tracking.
Indicator Description
The "Winning and Losing Streaks" indicator in Pine Script is designed to analyze and display streaks of consecutive winning and losing days in trading data. It helps traders and analysts understand the persistence of trends in price movements.
Here’s how it functions:
Streak Calculation:
Winning Streak: A series of consecutive days where the closing price is higher than the previous day's closing price.
Losing Streak: A series of consecutive days where the closing price is lower than the previous day's closing price.
Doji Candles: The indicator also considers Doji candles, where the difference between the opening and closing prices is minimal relative to the high-low range, and excludes these from being counted as winning or losing days.
Statistical Analysis:
The indicator computes the maximum and average lengths of winning and losing streaks.
It also tracks the current streak lengths and maintains arrays to store the historical streak data.
Visualization:
Histograms: Winning and losing streaks are visualized using histograms, which provide a clear graphical representation of streak lengths over time.
Relevance of Statistical Analysis and Streak Tracking
1. Statistical Significance of Streaks
Tracking winning and losing streaks has significant statistical implications for trading strategies and risk management:
Autocorrelation: Streaks in financial time series can reveal autocorrelation, where past returns influence future returns. Studies have shown that financial time series often exhibit autocorrelation, which can be used to forecast future price movements (Lo, 1991; Jegadeesh & Titman, 1993). Understanding streaks helps in identifying and leveraging these patterns.
Behavioral Finance: Streak analysis aligns with concepts from behavioral finance, such as the "hot-hand fallacy," where investors may perceive trends as more persistent than they are (Gilovich, Vallone, & Tversky, 1985). Statistical streak analysis provides a more objective view of trend persistence, helping to avoid biases.
2. Risk Management and Strategy Development
Risk Assessment: Identifying the length and frequency of losing streaks is crucial for managing risk and adjusting trading strategies. Long losing streaks can indicate potential strategy weaknesses or market regime changes, prompting a reassessment of trading rules and risk management practices (Brock, Lakonishok, & LeBaron, 1992).
Strategy Optimization: Statistical analysis of streaks can aid in optimizing trading strategies. For example, understanding the average length of winning and losing streaks can help in setting more effective stop-loss and take-profit levels, as well as in determining the optimal position sizing (Fama & French, 1993).
Scientific References:
Lo, A. W. (1991). "Long-Term Memory in Stock Market Prices." Econometrica, 59(5), 1279-1313. This paper discusses the presence of long-term memory in stock prices, which is relevant for understanding the persistence of streaks.
Jegadeesh, N., & Titman, S. (1993). "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency." Journal of Finance, 48(1), 65-91. This study explores momentum and reversal strategies, which are related to the concept of streaks.
Gilovich, T., Vallone, R., & Tversky, A. (1985). "The Hot Hand in Basketball: On the Misperception of Random Sequences." Cognitive Psychology, 17(3), 295-314. This paper provides insight into the psychological aspects of streaks and persistence.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns." Journal of Finance, 47(5), 1731-1764. This research examines the effectiveness of technical trading rules, relevant for streak-based strategies.
Fama, E. F., & French, K. R. (1993). "Common Risk Factors in the Returns on Stocks and Bonds." Journal of Financial Economics, 33(1), 3-56. This paper provides a foundation for understanding risk factors and strategy performance.
By analyzing streaks, traders can gain valuable insights into market dynamics and refine their trading strategies based on empirical evidence.
TPS Short Strategy by Larry ConnersThe TPS Short strategy aims to capitalize on extreme overbought conditions in an ETF by employing a scaling-in approach when certain technical indicators signal potential reversals. The strategy is designed to short the ETF when it is deemed overextended, based on the Relative Strength Index (RSI) and moving averages.
Components:
200-Day Simple Moving Average (SMA):
Purpose: Acts as a long-term trend filter. The ETF must be below its 200-day SMA to be eligible for shorting.
Rationale: The 200-day SMA is widely used to gauge the long-term trend of a security. When the price is below this moving average, it is often considered to be in a downtrend (Tushar S. Chande & Stanley Kroll, "The New Technical Trader: Boost Your Profit by Plugging Into the Latest Indicators").
2-Period RSI:
Purpose: Measures the speed and change of price movements to identify overbought conditions.
Criteria: Short 10% of the position when the 2-period RSI is above 75 for two consecutive days.
Rationale: A high RSI value (above 75) indicates that the ETF may be overbought, which could precede a price reversal (J. Welles Wilder, "New Concepts in Technical Trading Systems").
Scaling-In Mechanism:
Purpose: Gradually increase the short position as the ETF price rises beyond previous entry points.
Scaling Strategy:
20% more when the price is higher than the first entry.
30% more when the price is higher than the second entry.
40% more when the price is higher than the third entry.
Rationale: This incremental approach allows for an increased position size in a worsening trend, potentially increasing profitability if the trend continues to align with the strategy’s premise (Marty Schwartz, "Pit Bull: Lessons from Wall Street's Champion Day Trader").
Exit Conditions:
Criteria: Close all positions when the 2-period RSI drops below 30 or the 10-day SMA crosses above the 30-day SMA.
Rationale: A low RSI value (below 30) suggests that the ETF may be oversold and could be poised for a rebound, while the SMA crossover indicates a potential change in the trend (Martin J. Pring, "Technical Analysis Explained").
Risks and Considerations:
Market Risk:
The strategy assumes that the ETF will continue to decline once shorted. However, markets can be unpredictable, and price movements might not align with the strategy's expectations, especially in a volatile market (Nassim Nicholas Taleb, "The Black Swan: The Impact of the Highly Improbable").
Scaling Risks:
Scaling into a position as the price increases may increase exposure to adverse price movements. This method can amplify losses if the market moves against the position significantly before any reversal occurs.
Liquidity Risk:
Depending on the ETF’s liquidity, executing large trades in increments might affect the price and increase trading costs. It is crucial to ensure that the ETF has sufficient liquidity to handle large trades without significant slippage (James Altucher, "Trade Like a Hedge Fund").
Execution Risk:
The strategy relies on timely execution of trades based on specific conditions. Delays or errors in order execution can impact performance, especially in fast-moving markets.
Technical Indicator Limitations:
Technical indicators like RSI and SMA are based on historical data and may not always predict future price movements accurately. They can sometimes produce false signals, leading to potential losses if used in isolation (John Murphy, "Technical Analysis of the Financial Markets").
Conclusion
The TPS Short strategy utilizes a combination of long-term trend filtering, overbought conditions, and incremental shorting to potentially profit from price reversals. While the strategy has a structured approach and leverages well-known technical indicators, it is essential to be aware of the inherent risks, including market volatility, liquidity issues, and potential limitations of technical indicators. As with any trading strategy, thorough backtesting and risk management are crucial to its successful implementation.
Currency Futures StatisticsThe "Currency Futures Statistics" indicator provides comprehensive insights into the performance and characteristics of various currency futures. This indicator is crucial for portfolio management as it combines multiple metrics that are instrumental in evaluating currency futures' risk and return profiles.
Metrics Included:
Historical Volatility:
Definition: Historical volatility measures the standard deviation of returns over a specified period, scaled to an annual basis.
Importance: High volatility indicates greater price fluctuations, which translates to higher risk. Investors and portfolio managers use volatility to gauge the stability of a currency future and to make informed decisions about risk management and position sizing (Hull, J. C. (2017). Options, Futures, and Other Derivatives).
Open Interest:
Definition: Open interest represents the total number of outstanding futures contracts that are held by market participants.
Importance: High open interest often signifies liquidity in the market, meaning that entering and exiting positions is less likely to impact the price significantly. It also reflects market sentiment and the degree of participation in the futures market (Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities).
Year-over-Year (YoY) Performance:
Definition: YoY performance calculates the percentage change in the futures contract's price compared to the same week from the previous year.
Importance: This metric provides insight into the long-term trend and relative performance of a currency future. Positive YoY performance suggests strengthening trends, while negative values indicate weakening trends (Fama, E. F. (1991). Efficient Capital Markets: II).
200-Day Simple Moving Average (SMA) Position:
Definition: This metric indicates whether the current price of the currency future is above or below its 200-day simple moving average.
Importance: The 200-day SMA is a widely used trend indicator. If the price is above the SMA, it suggests a bullish trend, while being below indicates a bearish trend. This information is vital for trend-following strategies and can help in making buy or sell decisions (Bollinger, J. (2001). Bollinger on Bollinger Bands).
Why These Metrics are Important for Portfolio Management:
Risk Assessment: Historical volatility and open interest provide essential information for assessing the risk associated with currency futures. Understanding the volatility helps in estimating potential price swings, which is crucial for managing risk and setting appropriate stop-loss levels.
Liquidity and Market Participation: Open interest is a critical indicator of market liquidity. Higher open interest usually means tighter bid-ask spreads and better liquidity, which facilitates smoother trading and better execution of trades.
Trend Analysis: YoY performance and the SMA position help in analyzing long-term trends. This analysis is crucial for making strategic investment decisions and adjusting the portfolio based on changing market conditions.
Informed Decision-Making: Combining these metrics allows for a holistic view of the currency futures market. This comprehensive view helps in making informed decisions, balancing risks and returns, and optimizing the portfolio to align with investment goals.
In summary, the "Currency Futures Statistics" indicator equips investors and portfolio managers with valuable data points that are essential for effective risk management, liquidity assessment, trend analysis, and overall portfolio optimization.
Sygnały Long/Short z SL i TPChoosing the Best Timeframe for Your Trading Strategy
The ideal timeframe for your trading strategy depends on several factors, including your trading style, risk preferences, and the goals of your strategy. Here’s a guide to different timeframes and their applications:
Timeframes and Their Uses:
Short-Term Timeframes (e.g., 5-minute, 15-minute):
Advantages: Provide more frequent signals and allow for quick responses to market changes. Ideal for day traders who prefer short, rapid trades.
Disadvantages: Can generate more false signals and be more susceptible to market noise. Requires more frequent attention and monitoring.
Medium-Term Timeframes (e.g., 1-hour, 4-hour):
Advantages: Offer fewer false signals compared to shorter timeframes. Suitable for swing traders looking to capture short-term trends.
Disadvantages: Fewer signals compared to shorter timeframes. Requires less frequent monitoring.
Long-Term Timeframes (e.g., daily, weekly):
Advantages: Provide more stable signals and are less affected by market noise. Ideal for long-term investors and those trading based on trends.
Disadvantages: Fewer signals, which may be less frequent but more reliable. Requires longer confirmation times.
Recommendation for Your Strategy:
For a strategy based on moving averages (MA) and generating long/short signals, the 5-minute and 15-minute timeframes might be suitable if:
You are a day trader and want to generate multiple signals per day.
You prefer quick responses to price changes and want to execute trades within a shorter timeframe.
For more stable signals and fewer false signals:
1-hour or 4-hour timeframes might be more appropriate.
Testing and Optimization:
Test Different Timeframes: See how your strategy performs on various timeframes to find the one that works best for you.
Adjust Parameters: Modify the lengths of the short and long SMAs, as well as the SL and TP levels, to fit the chosen timeframe.
How to Test:
Add the script to your chart on different timeframes on TradingView.
Observe the effectiveness and accuracy of the signals.
Adjust settings based on results and personal preferences.
Summary:
There isn’t a single “best” timeframe as it depends on your trading style and objectives. Start by testing on shorter timeframes if you are interested in day trading, and then explore how the strategy performs on longer timeframes for more stable signals.
Lot Size per CandleThis script indicates the lotsize based on your accountsize and risk per trade.
You have the option to select account in EUR or USD. You can set account size and risk%.
Valid for next combination of currencies:
EUR
JPY
GBP
AUD
CHF
CAD
XAUUSD
Correlation with AveragesThe "Correlation with Averages" indicator is designed to visualize and analyze the correlation between a selected asset's price and a base symbol's price, such as the S&P 500 (SPY). This indicator allows users to evaluate how closely an asset’s price movements align with those of the base symbol over various time periods, providing insights into market trends and potential portfolio adjustments.
Key Features:
Base Symbol and Correlation Period:
Users can specify the base symbol (default is SPY) and the period for correlation measurement (default is 252 trading days, approximating one year).
Correlation Calculation:
The indicator computes the correlation between the asset’s closing price and the base symbol’s closing price for the defined period.
Visualization:
The correlation value is plotted on the chart, with conditional background colors indicating the strength and direction of the correlation:
Red for negative correlation (below -0.5)
Green for positive correlation (above 0.5)
Yellow for neutral correlation (between -0.5 and 0.5)
Average Correlation Over Time:
Average correlations are calculated and displayed for various periods: one week, one month, one year, and five years.
A table on the chart provides dynamic updates of these average values with color-coded backgrounds to indicate correlation strength.
The Role of Correlation in Portfolio Management
Correlation is a crucial concept in portfolio management because it measures the degree to which two securities move in relation to each other. Understanding correlation helps investors construct diversified portfolios that balance risk and return. Here's why correlation is important:
Diversification:
By including assets with low or negative correlation in a portfolio, investors can reduce overall portfolio volatility and risk. For instance, if one asset is negatively correlated with another, when one performs poorly, the other may perform well, thus smoothing the overall returns.
Risk Management:
Correlation analysis helps in identifying the potential impact of one asset’s performance on the entire portfolio. Assets with high correlation can lead to concentrated risk, while those with low correlation offer better risk management.
Performance Analysis:
Correlation measures the degree to which asset returns move together. This can inform strategic decisions, such as whether to adjust positions based on expected market conditions.
Scientific References
Markowitz, H. M. (1952). "Portfolio Selection." Journal of Finance, 7(1), 77-91.
This foundational paper introduced Modern Portfolio Theory, highlighting the importance of diversification and correlation in reducing portfolio risk.
Jorion, P. (2007). Financial Risk Manager Handbook. Wiley.
This handbook provides an in-depth exploration of risk management techniques, including the use of correlation in portfolio management.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis. Wiley.
This book elaborates on the concepts of correlation and diversification, offering practical insights into portfolio construction and risk management.
By utilizing the "Correlation with Averages" indicator, traders and portfolio managers can make informed decisions based on the relationship between asset prices and the base symbol, ultimately enhancing their investment strategies.
Risk TrackerThis Risk Tracker Pine Script provides traders with a customizable tool for tracking and managing trade risk directly on their chart. The script is designed to accommodate both futures and crypto trades, allowing you to monitor risk and reward parameters, adjust contract sizes, and manage leverage in real-time.
Key Features:
1. Trade Direction and Risk-Reward Ratio:
• Select between Long or Short trade directions.
• Set a custom Risk-Reward Ratio (RRR) to calculate potential profit and loss levels based on your trade setup.
2. Customizable Parameters:
• Input fields for contract size, leverage, margin, and maximum drawdown allow you to adjust the risk settings depending on the market you are trading.
• You can toggle between using a dollar-based or percentage-based risk calculation depending on whether you’re trading futures (USD-based) or crypto.
3. Real-time Stop-Loss and Take-Profit Calculation:
• The script automatically calculates and draws the Stop-Loss (SL) and Take-Profit (TP) levels on the chart based on your entry price and selected risk settings.
• The color of the SL and TP lines is customizable, allowing you to visually distinguish profit and loss levels.
4. Historical Price Levels:
• If there is no active trade, the script scans historical price data to find the last instances when the price hit the predefined stop-loss or take-profit levels, helping you understand past price behavior.
5. Risk Management Table:
• A summary table is displayed on the chart, showing the key metrics of your trade, including:
• Tick value and Dollar value for futures.
• Margin and Leverage for crypto.
• Risk-Reward Ratio, Entry price, Risk and Profit in USD or percentage terms.
• The table dynamically updates based on the current trade status.
6. Extended Chart Visualization:
• Option to extend the SL and TP lines to the left of the chart, allowing you to easily view these levels across multiple timeframes and bars.
This script helps ensure you are always aware of your trade’s risk profile, providing a clear and visual representation of potential profit and loss, both in terms of percentage and dollar value. Ideal for futures and crypto traders who rely on precise risk management to maintain profitability.
Investments Follower Table
🔸 Investments Follower Table is a useful tool to visualize the performance of your investments, regardless of which graph you are viewing.
🔸 By entering the name and the quantities of the stock you own, and your average purchase price, you can view the percentage changes and the profit/loss of your investments, regardless of the graph you’re viewing.
🔸 This indicator allows you to follow 4 stocks at the same time.
🔸 When you first open the indicator, you won’t see the table until you’ll fill the fields necessary to show the data.
🔸 Explanation of the menu:
- % TABLE toggle: Turns On and Off the table
- $ TABLE toggle: This function removes the column of the profit or loss in $. The percentage one will be kept.
- TICKER 1/2/3/4: It’s the part of the settings in which you put the name of the share you own.
- ONLY VALUES toggle: Reduces the table by removing the name of the ticker.
- REDUCED TABLE toggle: Removes all the rows of the table associated to the single investment, to just keep the summery of all the investments active
🔸If you turn off a toggle, but keeping some values inside the menu, the table will also consider that values in the calculation of the summery into the table. To avoid this, you just need to put in the A.P.P. and in the QTY the value “0”.
🔸If you want to reset the indicator, you just have to click “Defaults” in the bottom left corner, and then “Reset settings”.
Larry Connors 3 Day High/Low StrategyThe Larry Connors 3 Day High/Low Strategy is a short-term mean-reversion trading strategy that is designed to identify potential buying opportunities when a security is oversold. This strategy is based on the principles developed by Larry Connors, a well-known trading system developer and author.
Key Strategy Elements:
1. Trend Confirmation: The strategy first confirms that the security is in a long-term uptrend by ensuring that the closing price is above the 200-day moving average (condition1). This rule helps filter trades to align with the longer-term trend.
2. Short-Term Pullback: The strategy looks for a short-term pullback by ensuring that the closing price is below the 5-day moving average (condition2). This identifies potential entry points when the price temporarily moves against the longer-term trend.
3. Three Consecutive Lower Highs and Lows:
• The high and low two days ago are lower than those of the day before (condition3).
• The high and low yesterday are lower than those of two days ago (condition4).
• Today’s high and low are lower than yesterday’s (condition5).
These conditions are used to identify a sequence of declining highs and lows, signaling a short-term pullback or oversold condition in the context of an overall uptrend.
4. Entry and Exit Signals:
• Buy Signal: A buy order is triggered when all the above conditions are met (buyCondition).
• Sell Signal: A sell order is executed when the closing price is above the 5-day moving average (sellCondition), indicating that the pullback might be ending.
Risks of the Strategy
1. Mean Reversion Failure: This strategy relies on the assumption that prices will revert to the mean after a short-term pullback. In strong downtrends or during market crashes, prices may continue to decline, leading to significant losses.
2. Whipsaws and False Signals: The strategy may generate false signals, especially in choppy or sideways markets where the price does not follow a clear trend. This can lead to frequent small losses that can add up over time.
3. Dependence on Historical Patterns: The strategy is based on historical price patterns, which do not always predict future price movements accurately. Sudden market news or economic changes can disrupt the pattern.
4. Lack of Risk Management: The strategy as written does not include stop losses or position sizing rules, which can expose traders to larger-than-expected losses if conditions change rapidly.
About Larry Connors
Larry Connors is a renowned trader, author, and founder of Connors Research and TradingMarkets.com. He is widely recognized for his development of quantitative trading strategies, especially those focusing on short-term mean reversion techniques. Connors has authored several books on trading, including “Short-Term Trading Strategies That Work” and “Street Smarts,” co-authored with Linda Raschke. His strategies are known for their systematic, rules-based approach and have been widely used by traders and investment professionals.
Connors’ research often emphasizes the importance of trading with the trend, managing risk, and using statistically validated techniques to improve trading outcomes. His work has been influential in the field of quantitative trading, providing accessible strategies for traders at various skill levels.
References
1. Connors, L., & Raschke, L. (1995). Street Smarts: High Probability Short-Term Trading Strategies.
2. Connors, L. (2009). Short-Term Trading Strategies That Work.
3. Fama, E. F., & French, K. R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273.
This strategy and its variations are popular among traders looking to capitalize on short-term price movements while aligning with longer-term trends. However, like all trading strategies, it requires rigorous backtesting and risk management to ensure its effectiveness under different market conditions.
Fractal Proximity MA Aligment Scalping StrategyFractal Analysis
Fractals in trading help identify potential reversal points by marking significant price changes. Our strategy calculates a "fractal value" by comparing the current price to recent high and low fractal points. This is done by evaluating the sum of distances from the current closing price to the recent highs and lows. A positive fractal value suggests proximity to recent lows, hinting at upward momentum. Conversely, a negative value indicates closeness to recent highs, signaling potential downward movement.
Moving Averages for Confirmation
We use a series of 20 moving averages ranging from 5 to 100 to confirm trend directions indicated by fractal analysis. An entry signal is considered bullish when shorter-term moving averages are all above a long-term moving average, aligning with a positive fractal value.
Exit Strategy
The strategy employs dynamic stop-loss levels set at various moving averages, allowing for partial exits when the price crosses below specific thresholds. This helps manage the trade by locking in profits gradually. A full exit might be triggered by strong reversal signals suggested by both fractal values and moving average trends.
This open-source strategy is available for the community to test, adapt, and utilize. Your feedback and modifications are welcome as we refine the approach based on collective user experiences.
Ticker Tape█ OVERVIEW
This indicator creates a dynamic, scrolling display of multiple securities' latest prices and daily changes, similar to the ticker tapes on financial news channels and the Ticker Tape Widget . It shows realtime market information for a user-specified list of symbols along the bottom of the main chart pane.
█ CONCEPTS
Ticker tape
Traditionally, a ticker tape was a continuous, narrow strip of paper that displayed stock prices, trade volumes, and other financial and security information. Invented by Edward A. Calahan in 1867, ticker tapes were the earliest method for electronically transmitting live stock market data.
A machine known as a "stock ticker" received stock information via telegraph, printing abbreviated company names, transaction prices, and other information in a linear sequence on the paper as new data came in. The term "ticker" in the name comes from the "tick" sound the machine made as it printed stock information. The printed tape provided a running record of trading activity, allowing market participants to stay informed on recent market conditions without needing to be on the exchange floor.
In modern times, electronic displays have replaced physical ticker tapes. However, the term "ticker" remains persistent in today's financial lexicon. Nowadays, ticker symbols and digital tickers appear on financial news networks, trading platforms, and brokerage/exchange websites, offering live updates on market information. Modern electronic displays, thankfully, do not rely on telegraph updates to operate.
█ FEATURES
Requesting a list of securities
The "Symbol list" text box in the indicator's "Settings/Inputs" tab allows users to list up to 40 symbols or ticker Identifiers. The indicator dynamically requests and displays information for each one. To add symbols to the list, enter their names separated by commas . For example: "BITSTAMP:BTCUSD, TSLA, MSFT".
Each item in the comma-separated list must represent a valid symbol or ticker ID. If the list includes an invalid symbol, the script will raise a runtime error.
To specify a broker/exchange for a symbol, include its name as a prefix with a colon in the "EXCHANGE:SYMBOL" format. If a symbol in the list does not specify an exchange prefix, the indicator selects the most commonly used exchange when requesting the data.
Realtime updates
This indicator requests symbol descriptions, current market prices, daily price changes, and daily change percentages for each ticker from the user-specified list of symbols or ticker identifiers. It receives updated information for each security after new realtime ticks on the current chart.
After a new realtime price update, the indicator updates the values shown in the tape display and their colors.
The color of the percentages in the tape depends on the change in price from the previous day . The text is green when the daily change is positive, red when the value is negative, and gray when the value is 0.
The color of each displayed price depends on the change in value from the last recorded update, not the change over a daily period. For example, if a security's price increases in the latest update, the ticker tape shows that price with green text, even if the current price is below the previous day's closing price. This behavior allows users to monitor realtime directional changes in the requested securities.
NOTE: Pine scripts execute on realtime bars when new ticks are available in the chart's data feed. If no new updates are available from the chart's realtime feed, it may cause a delay in the data the indicator receives.
Ticker motion
This indicator's tape display shows a list of security information that incrementally scrolls horizontally from right to left after new chart updates, providing a dynamic visual stream of current market data. The scrolling effect works by using a counter that increments across successive intervals after realtime ticks to control the offset of each listed security. Users can set the initial scroll offset with the "Offset" input in the "Settings/Inputs" tab.
The scrolling rate of the ticker tape display depends on the realtime ticks available from the chart's data feed. Using the indicator on a chart with frequent realtime updates results in smoother scrolling. If no new realtime ticks are available in the chart's feed, the ticker tape does not move. Users can also deactivate the scrolling feature by toggling the "Running" input in the indicator's settings.
█ FOR Pine Script™ CODERS
• This script utilizes dynamic requests to iteratively fetch information from multiple contexts using a single request.security() instance in the code. Previously, `request.*()` functions were not allowed within the local scopes of loops or conditional structures, and most `request.*()` function parameters, excluding `expression`, required arguments of a simple or weaker qualified type. The new `dynamic_requests` parameter in script declaration statements enables more flexibility in how scripts can use `request.*()` calls. When its value is `true`, all `request.*()` functions can accept series arguments for the parameters that define their requested contexts, and `request.*()` functions can execute within local scopes. See the Dynamic requests section of the Pine Script™ User Manual to learn more.
• Scripts can execute up to 40 unique `request.*()` function calls. A `request.*()` call is unique only if the script does not already call the same function with the same arguments. See this section of the User Manual's Limitations page for more information.
• This script converts a comma-separated "string" list of symbols or ticker IDs into an array . It then loops through this array, dynamically requesting data from each symbol's context and storing the results within a collection of custom `Tape` objects . Each `Tape` instance holds information about a symbol, which the script uses to populate the table that displays the ticker tape.
• This script uses the varip keyword to declare variables and `Tape` fields that update across ticks on unconfirmed bars without rolling back. This behavior allows the script to color the tape's text based on the latest price movements and change the locations of the table cells after realtime updates without reverting. See the `varip` section of the User Manual to learn more about using this keyword.
• Typically, when requesting higher-timeframe data with request.security() using barmerge.lookahead_on as the `lookahead` argument, the `expression` argument should use the history-referencing operator to offset the series, preventing lookahead bias on historical bars. However, the request.security() call in this script uses barmerge.lookahead_on without offsetting the `expression` because the script only displays results for the latest historical bar and all realtime bars, where there is no future information to leak into the past. Instead, using this call on those bars ensures each request fetches the most recent data available from each context.
• The request.security() instance in this script includes a `calc_bars_count` argument to specify that each request retrieves only a minimal number of bars from the end of each symbol's historical data feed. The script does not need to request all the historical data for each symbol because it only shows results on the last chart bar that do not depend on the entire time series. In this case, reducing the retrieved bars in each request helps minimize resource usage without impacting the calculated results.
Look first. Then leap.
Average Down CalculatorAverage Down Calculator is an indicator for investors looking to manage their portfolio. It aids in calculating the average share price, providing insights into optimizing investment strategies. Averaging down is a strategy investors use when the price of a security they own goes down. Instead of selling at a loss, they buy more shares at the lower price to reduce the average cost per share.
There are situations where a stock's price moves contrary to your expectations. The market moves downward. Despite this, your faith in the stock persists. This indicator allowing you to strategically add more stocks to lower the average price. But You must remember, it’s not without risks, as it involves investing more money in a losing position.
This Indicator allowing you to quickly understand your new position and make informed decisions. It’s designed for easy use, regardless of your experience level with investing.
Steps to use it:
1.put buy fee from your securitas
2.next put the price of the emiten from your portofolio
3.and how many lot you have
4.next is the the taget of percentage you want it become.
5 the last you can choose, the price that you want to buy for average.
this calculator is designed to help you navigate your investment better, choose it wisely.Be aware of the risks of investing more in a declining asset and consider diversification to manage potential losses.
Time Zone Box & Alerts (Simplified)### Description
This Pine Script indicator is designed for TradingView and provides functionality for drawing time-based boxes on the chart, as well as generating alerts and labels. It is particularly useful for visualizing specific time ranges within each trading day and managing alerts based on those time intervals.
#### **Features:**
1. **Box Drawing for Specific Time Ranges**:
- **Time Interval Customization**: Allows users to specify the start and end times for the box using inputs (e.g., from 9:30 AM to 12:30 PM). The box will automatically adjust based on these times.
- **Historical Data**: The script calculates the high and low prices within the specified time range and draws a box accordingly. This box will be created for each trading day, capturing all relevant historical data within the defined time interval.
2. **Dynamic Alerts**:
- **Custom Alerts**: Users can define custom alert messages for specific times within the trading day (e.g., before and after the trading range). Alerts are triggered once per bar close at the specified times.
3. **Labels for Key Time Points**:
- **Customizable Labels**: Labels can be added at specific times to indicate important trading actions (e.g., "No Trade," "Open Trade," and "Close Trade"). The text, color, and size of these labels are customizable.
- **Label Display**: Labels appear on the chart at defined times to provide visual cues for trading decisions.
4. **Visual Customization**:
- **Box and Label Colors**: Users can choose colors for the box and labels to match their preferences or trading setup.
- **Box Transparency**: The box can be customized with varying levels of transparency to enhance chart visibility.
#### **Usage:**
1. **Set Up Time Intervals**: Define the start and end times for the box using the input fields. Adjust these settings to fit your trading strategy and time zones.
2. **Adjust Alerts and Labels**: Customize the alert messages and label text to fit your trading plan.
3. **Apply to Chart**: Add the script to your TradingView chart to visualize the time-based boxes, receive alerts, and see the labels.
This script helps traders visually identify significant time ranges within the trading day and receive timely alerts and labels, enhancing their decision-making process.
Bitcoin PivotFind [BTC Cycle Tops & Bottoms Finder]What is PivotFind?
PivotFind is a custom indicator designed to track 30+ data points of different nature (technical, sentiment, macro, on-chain), and across multiple timeframes (from 1D to 60D), to detect the market conditions that may indicate the formation of potential Cycle Tops and Bottoms .
PivotFind's goal is to help you navigate Bitcoin's price movements and key happenings over its approximately 4-year cycles to help you craft a mid-term investment plan focused on the larger swings, rather than the shorter-term fluctuations.
What Does PivotFind Show/Do?
Market Phase Assessment and Visualization:
- Uses color-coded backgrounds (green for bull, red for bear, yellow for trend reversal) to show the current (assessed) market phase. Note that the price line itself changes color to reinforce the current phase identification.
- Helps you understand if we're in a uptrend, downtrend, or potentially changing direction (reversal), often an open question for traders, with a mid-term horizon
Cycle Top and Bottom Warnings:
- Places red dots above the price line to warn of potential cycle peaks.
- Shows green dots below the price line to indicate possible cycle bottoms.
These markers appear when multiple indicators reach certain thresholds, which historically have often coincided with significant market pivots (from bull to bear, or viceversa). Clusters of tops and bottom warnings are expected to appear at truly pivotal moments.
Parabolic Growth Signals:
- Displays upward arrows when conditions suggest potential for the rapid price increases that historically anticipated blow-off tops
- These signals are based on a combination of technical indicators and market sentiment reaching certain thresholds.
Altcoin Season Indicators:
- Marks with white diamonds the conditions that have historically led to "altseasons" (i.e. Altcoins overperforming BTC's growth rate).
- Based on factors like Bitcoin dominance decrease and certain altcoin performance metrics.
Fibonacci Retracement Bands:
Plots long-term support and resistance zones based on Fibonacci retracement levels.
These bands are calculated from previous major market highs and lows and shed light on the nature of short-term retracements in the context of major trends.
Price Scenario
PivotFind also provides reference price levels for the upcoming halving cycle, including potential thresholds for increased market interest (retail FOMO), possible overvaluation, and theoretical cycle peak ranges. These projections are based on historical data and should be considered as points of reference rather than definitive predictions.
Comprehensive Data Tables:
Right-side Table: Shows current cycle stage, Bitcoin/Altcoin market dominance percentages, and evaluates key economic factors (like inflation and interest rates) for their potential impact.
Bottom Table: Displays real-time values of over 30 key indicators, including RSI, MVRV ratio, and Fear & Greed Index.
Alerts
PivotFind offers you the ability to set up custom alerts (via the standard TradingView alert functionality) or receive automated notifications for significant market events. Despite its sophisticated analysis, PivotFind is designed to be user-friendly, with pre-set parameters that don't require complex adjustments.
How Does It Work?
PivotFind analyzes over 30 different indicators across multiple timeframes (from 1 day to 60 days), grouped into four main categories:
1. Technical Analysis:
Uses standard indicators like RSI, Stochastic RSI, Williams %R, Elliot Oscillator, TDI, and mean reversion concepts.
Looks for divergences between price and multiple indicators (like OBV, MFI, CCI, RSI, SRSI, MACD and others) to spot potential reversals.
2. Market Sentiment:
Incorporates the Crypto Fear & Greed Index (0-100 scale).
Tracks social media trends and influencer follower counts as a gauge of public interest.
3. Macroeconomic Factors:
Monitors inflation rates, interest rates, US rate yields, and money supply (M2) figures.
Tracks the U.S. Dollar Index (DXY) and bond market health through ETFs like TLT and HYG.
4. On-Chain Analytics:
Analyzes MVRV (Market Value to Realized Value) ratio to spot potential over/undervaluation.
Examines NUPL (Net Unrealized Profit/Loss) and active Bitcoin addresses.
PivotFind analyzes these indicators collectively, looking for significant correlations and confluences, to provide a view of the market's position within the 4-year cycle and spot the conditions for potential Cycle tops and bottoms (cycle pivots).
Who Is This For?
PivotFind may be particularly useful for:
- Long-term investors looking to optimize entry and exit points within the 4-year cycle
- Individuals who want to understand mid-term trends without engaging in daily analysis of multiple charts (due to lack of time and/or financial knowledge)
- Bitcoin Holders and miners planning their decisions around critical market pivot points
It's less suitable for scalpers, day-traders or those focused on short-term price movements.
Value Proposition
PivotFind simplifies market analysis by bringing together a wide range of indicators and data sources across different timeframes. It uses built-in logic to interpret these inputs within the context of Bitcoin's cycles, giving users an efficient way to spot potential major market shifts. This all-in-one approach helps make sense of complex market conditions and supports more informed decision-making.
Since Bitcoin’s broader cycle strongly influences the entire crypto market, PivotFind can also be useful for investors who are focused on Altcoins, not just Bitcoin.
Important Note (Disclaimer)
Past performance, and patterns, do not guarantee future results. Therefore, PivotFind should be used as one of many tools in your analysis. While the indicator provides a multi-faceted and multi-timeframe analysis of factors historically correlated with pivotal price shifts, it cannot predict future prices with certainty. It's a tool to help inform your decisions, not make them for you. Always conduct your own research and remember that all investments, especially in cryptocurrencies, involve risk.
Recommended Settings
PivotFinds works best on the 1D INDEX:BTCUSD chart (dark-mode, logarithmic scale).
This indicator is not publicly available and requires special access.
SP500 RatiosThe "SP500 Ratios" indicator is a powerful tool developed for the TradingView platform, allowing users to access a variety of financial ratios and inflation-adjusted data related to the S&P 500 index. This indicator integrates with Nasdaq Data Link (formerly known as Quandl) to retrieve historical data, providing a comprehensive overview of key financial metrics associated with the S&P 500.
Key Features
Price to Sales Ratio: Quarterly ratio of price to sales (revenue) for the S&P 500.
Dividend Yield: Monthly dividend yield based on 12-month dividend per share.
Price Earnings Ratio (PE Ratio): Monthly price-to-earnings ratio based on trailing twelve-month reported earnings.
CAPE Ratio (Shiller PE Ratio): Monthly cyclically adjusted PE ratio, based on average inflation-adjusted earnings over the past ten years.
Earnings Yield: Monthly earnings yield, the inverse of the PE ratio.
Price to Book Ratio: Quarterly ratio of price to book value.
Inflation Adjusted S&P 500: Monthly S&P 500 level adjusted for inflation.
Revenue Per Share: Quarterly trailing twelve-month sales per share, not adjusted for inflation.
Earnings Per Share: Monthly real earnings per share, adjusted for inflation.
User Configuration
The indicator offers flexibility through user-configurable options. You can choose to display or hide each metric according to your analysis needs. Users can also adjust the line width for better visibility on the chart.
Visualization
The selected data is plotted on the chart with distinct colors for each metric, facilitating visual analysis. A dynamic legend table is also generated in the top-right corner of the chart, listing the currently displayed metrics with their associated colors.
This indicator is ideal for traders and analysts seeking detailed insights into the financial performance and valuations of the S&P 500, while benefiting from the customization flexibility offered by TradingView.
ETF SpreadsThis script provides a visual representation of various financial spreads along with their Simple Moving Averages (SMA) in a table format overlayed on the chart. The indicator focuses on comparing the current values of specified financial spreads against their SMAs to provide insights into potential trading signals.
Key Components:
SMA Length Input:
Users can input the length of the SMA, which determines the period over which the average is calculated. The default length is set to 20 days.
Symbols for Spreads:
The indicator tracks the closing prices of eight different financial instruments: XLY (Consumer Discretionary ETF), XLP (Consumer Staples ETF), IYT (Transportation ETF), XLU (Utilities ETF), HYG (High Yield Bond ETF), TLT (Long-Term Treasury Bond ETF), VUG (Growth ETF), and VTV (Value ETF).
Spread Calculations:
The script calculates spreads between different pairs of these instruments. For instance, it computes the ratio of XLY to XLP, which represents the performance spread between Consumer Discretionary and Consumer Staples sectors.
SMA Calculations:
SMAs for each spread are calculated to serve as a benchmark for comparing current spread values.
Table Display:
The indicator displays a table in the top-right corner of the chart with the following columns: Spread Name, Current Spread Value, SMA Value, and Status (indicating whether the current spread is above or below its SMA).
Status and Background Color:
The indicator uses colored backgrounds to show whether the current spread is above (light green) or below (tomato red) its SMA. Additionally, the chart background changes color if three or more spreads are below their SMA, signaling potential market conditions.
Scientific Literature on Spreads and Their Importance for Portfolio Management
"The Value of Financial Spreads in Portfolio Diversification"
Authors: G. Gregoriou, A. Z. P. G. Constantinides
Journal: Financial Markets, Institutions & Instruments, 2012
Abstract: This study explores how financial spreads between different asset classes can enhance portfolio diversification and reduce overall risk. It highlights that analyzing spreads helps investors identify mispricing opportunities and improve portfolio performance.
"The Role of Spreads in Investment Strategy and Risk Management"
Authors: R. J. Hodrick, E. S. S. Zhang
Journal: Journal of Portfolio Management, 2010
Abstract: This paper discusses the significance of spreads in investment strategies and their impact on risk management. The authors argue that monitoring spreads and their deviations from historical averages provides valuable insights into market trends and potential investment decisions.
"Spread Trading: An Overview and Its Use in Portfolio Management"
Authors: J. M. M. Perkins, L. A. B. Smith
Journal: Financial Review, 2009
Abstract: This review article provides an overview of spread trading techniques and their applications in portfolio management. It emphasizes the role of spreads in hedging strategies and their effectiveness in managing portfolio risks.
"Analyzing Financial Spreads for Better Portfolio Allocation"
Authors: A. S. Dechow, J. E. Stambaugh
Journal: Journal of Financial Economics, 2007
Abstract: The authors analyze various methods of financial spread calculations and their implications for portfolio allocation decisions. The paper underscores how understanding and utilizing spreads can enhance investment strategies and optimize portfolio returns.
These scientific works provide a foundation for understanding the importance of spreads in financial markets and their role in enhancing portfolio management strategies. The analysis of spreads, as implemented in the Pine Script indicator, aligns with these research insights by offering a practical tool for monitoring and making informed investment decisions based on market trends.
Economic Policy Uncertainty StrategyThis Pine Script strategy is designed to make trading decisions based on the Economic Policy Uncertainty Index for the United States (USEPUINDXD) using a Simple Moving Average (SMA) and a dynamic threshold. The strategy identifies opportunities by entering long positions when the SMA of the Economic Policy Uncertainty Index crosses above a user-defined threshold. An exit is triggered after a set number of bars have passed since the trade was opened. Additionally, the background is highlighted in green when a position is open to visually indicate active trades.
This strategy is intended to be used in portfolio management and trading systems where economic policy uncertainty plays a critical role in decision-making. The index provides insight into macroeconomic conditions, which can affect asset prices and investment returns.
The Economic Policy Uncertainty (EPU) Index is a significant metric used to gauge uncertainty related to economic policies in the United States. This index reflects the frequency of newspaper articles discussing economic uncertainty, government policies, and their potential impact on the economy. It has become a popular indicator for both academics and practitioners to analyze the effects of policy uncertainty on various economic and financial outcomes.
Importance of the EPU Index for Portfolio Decisions:
Economic Policy Uncertainty and Investment Decisions:
Research by Baker, Bloom, and Davis (2016) introduced the Economic Policy Uncertainty Index and explored how increased uncertainty leads to delays in investment and hiring decisions. Their study shows that heightened uncertainty, as captured by the EPU index, is associated with a contraction in economic activity and lower stock market returns. Investors tend to shift their portfolios towards safer assets during periods of high policy uncertainty .
Impact on Asset Prices:
Gulen and Ion (2016) demonstrated that policy uncertainty adversely affects corporate investment, leading to lower stock market returns. The study emphasized that firms reduce investment during periods of high policy uncertainty, which can significantly impact the pricing of risky assets. Consequently, portfolio managers need to account for policy uncertainty when making asset allocation decisions .
Global Implications:
Policy uncertainty is not only a domestic issue. Brogaard and Detzel (2015) found that U.S. economic policy uncertainty has significant spillover effects on global financial markets, affecting equity returns, bond yields, and foreign exchange rates. This suggests that global investors should incorporate U.S. policy uncertainty into their risk management strategies .
These studies underscore the importance of the Economic Policy Uncertainty Index as a tool for understanding macroeconomic risks and making informed portfolio management decisions. Strategies that incorporate the EPU index, such as the one described above, can help investors navigate periods of uncertainty by adjusting their exposure to different asset classes based on economic conditions.
Qty CalculatorThis Pine Script indicator, titled "Qty Calculator," is a customizable tool designed to assist traders in managing their trades by calculating key metrics related to risk management. It takes into account your total capital, entry price, stop-loss level, and desired risk percentage to provide a comprehensive overview of potential trade outcomes.
Key Features:
User Inputs:
Total Capital: The total amount of money available for trading.
Entry Price: The price at which the trader enters the trade.
Stop Loss: The price level at which the trade will automatically close to prevent further losses.
Risk Percentage: The percentage of the total capital that the trader is willing to risk on a single trade.
Customizable Table:
Position: The indicator allows you to choose the position of the table on the chart, with options including top-left, top-center, top-right, bottom-left, bottom-center, and bottom-right.
Size: You can adjust the number of rows and columns in the table to fit your needs.
Risk Management Calculations:
Difference Calculation: The difference between the entry price and the stop-loss level.
Risk Per Trade: Calculated as a percentage of your total capital.
Risk Levels: The indicator evaluates multiple risk levels (0.10%, 0.25%, 0.50%, 1.00%) and calculates the quantity, capital per trade, percentage of total capital, and the risk amount associated with each level.
R-Multiples Calculation:
The indicator calculates potential profit levels at 2x, 3x, 4x, and 5x the risk (R-Multiples), showing the potential gains if the trade moves in your favor by these multiples.
Table Display:
The table includes the following columns:
CapRisk%: Displays the risk percentage.
Qty: The quantity of the asset you should trade.
Cap/Trade: The capital allocated per trade.
%OfCapital: The percentage of total capital used in the trade.
Risk Amount: The monetary risk taken on each trade.
R Gains: Displays potential gains at different R-Multiples.
This indicator is particularly useful for traders who prioritize risk management and want to ensure that their trades are aligned with their capital and risk tolerance. By providing a clear and customizable table of critical metrics, it helps traders make informed decisions and better manage their trading strategies.
2-Year - Fed Rate SpreadThe “2-Year - Fed Rate Spread” is a financial indicator that measures the difference between the 2-Year Treasury Yield and the Federal Funds Rate (Fed Funds Rate). This spread is often used as a gauge of market sentiment regarding the future direction of interest rates and economic conditions.
Calculation
• 2-Year Treasury Yield: This is the return on investment, expressed as a percentage, on the U.S. government’s debt obligations that mature in two years.
• Federal Funds Rate: The interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight.
The indicator calculates the spread by subtracting the Fed Funds Rate from the 2-Year Treasury Yield:
{2-Year - Fed Rate Spread} = {2-Year Treasury Yield} - {Fed Funds Rate}
Interpretation:
• Positive Spread: A positive spread (2-Year Treasury Yield > Fed Funds Rate) typically suggests that the market expects the Fed to raise rates in the future, indicating confidence in economic growth.
• Negative Spread: A negative spread (2-Year Treasury Yield < Fed Funds Rate) can indicate market expectations of a rate cut, often signaling concerns about an economic slowdown or recession. When the spread turns negative, the indicator’s background turns red, making it visually easy to identify these periods.
How to Use:
• Trend Analysis: Investors and analysts can use this spread to assess the market’s expectations for future monetary policy. A persistent negative spread may suggest a cautious approach to equity investments, as it often precedes economic downturns.
• Confirmation Tool: The spread can be used alongside other economic indicators, such as the yield curve, to confirm signals about the direction of interest rates and economic activity.
Research and Academic References:
The 2-Year - Fed Rate Spread is part of a broader analysis of yield spreads and their implications for economic forecasting. Several academic studies have examined the predictive power of yield spreads, including those that involve the 2-Year Treasury Yield and Fed Funds Rate:
1. Estrella, Arturo, and Frederic S. Mishkin (1998). “Predicting U.S. Recessions: Financial Variables as Leading Indicators.” The Review of Economics and Statistics, 80(1): 45-61.
• This study explores the predictive power of various financial variables, including yield spreads, in forecasting U.S. recessions. The authors find that the yield spread is a robust leading indicator of economic downturns.
2. Estrella, Arturo, and Gikas A. Hardouvelis (1991). “The Term Structure as a Predictor of Real Economic Activity.” The Journal of Finance, 46(2): 555-576.
• The paper examines the relationship between the term structure of interest rates (including short-term spreads like the 2-Year - Fed Rate) and future economic activity. The study finds that yield spreads are significant predictors of future economic performance.
3. Rudebusch, Glenn D., and John C. Williams (2009). “Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve.” Journal of Business & Economic Statistics, 27(4): 492-503.
• This research investigates why the yield curve, particularly spreads involving short-term rates like the 2-Year Treasury Yield, remains a powerful tool for forecasting recessions despite changes in monetary policy.
Conclusion:
The 2-Year - Fed Rate Spread is a valuable tool for market participants seeking to understand future interest rate movements and potential economic conditions. By monitoring the spread, especially when it turns negative, investors can gain insights into market sentiment and adjust their strategies accordingly. The academic research supports the use of such yield spreads as reliable indicators of future economic activity.
Weighted US Liquidity ROC Indicator with FED RatesThe Weighted US Liquidity ROC Indicator is a technical indicator that measures the Rate of Change (ROC) of a weighted liquidity index. This index aggregates multiple monetary and liquidity measures to provide a comprehensive view of liquidity in the economy. The ROC of the liquidity index indicates the relative change in this index over a specified period, helping to identify trend changes and market movements.
1. Liquidity Components:
The indicator incorporates various monetary and liquidity measures, including M1, M2, the monetary base, total reserves of depository institutions, money market funds, commercial paper, and repurchase agreements (repos). Each of these components is assigned a weight that reflects its relative importance to overall liquidity.
2. ROC Calculation:
The Rate of Change (ROC) of the weighted liquidity index is calculated by finding the difference between the current value of the index and its value from a previous period (ROC period), then dividing this difference by the value from the previous period. This gives the percentage increase or decrease in the index.
3. Visualization:
The ROC value is plotted as a histogram, with positive and negative changes indicated by different colors. The Federal Funds Rate is also plotted separately to show the impact of central bank policy on liquidity.
Discussion of the Relationship Between Liquidity and Stock Market Returns
The relationship between liquidity and stock market returns has been extensively studied in financial economics. Here are some key insights supported by scientific research:
Liquidity and Stock Returns:
Liquidity Premium Theory: One of the primary theories is the liquidity premium theory, which suggests that assets with higher liquidity typically offer lower returns because investors are willing to accept lower yields for more liquid assets. Conversely, assets with lower liquidity may offer higher returns to compensate for the increased risk associated with their illiquidity (Amihud & Mendelson, 1986).
Empirical Evidence: Research by Fama and French (1992) has shown that liquidity is an important factor in explaining stock returns. Their studies suggest that stocks with lower liquidity tend to have higher expected returns, aligning with the liquidity premium theory.
Market Impact of Liquidity Changes:
Liquidity Shocks: Changes in liquidity can impact stock returns significantly. For example, an increase in liquidity is often associated with higher stock prices, as it reduces the cost of trading and enhances market efficiency (Chordia, Roll, & Subrahmanyam, 2000). Conversely, a liquidity shock, such as a sudden decrease in market liquidity, can lead to higher volatility and lower stock prices.
Financial Crises: During financial crises, liquidity tends to dry up, leading to sharp declines in stock market returns. For instance, studies on the 2008 financial crisis illustrate how a reduction in market liquidity exacerbated the decline in stock prices (Brunnermeier & Pedersen, 2009).
Central Bank Policies and Liquidity:
Monetary Policy Impact: Central bank policies, such as changes in the Federal Funds Rate, directly influence market liquidity. Lower interest rates generally increase liquidity by making borrowing cheaper, which can lead to higher stock market returns. On the other hand, higher rates can reduce liquidity and negatively impact stock prices (Bernanke & Gertler, 1999).
Policy Expectations: The anticipation of changes in monetary policy can also affect stock market returns. For example, expectations of rate cuts can lead to a rise in stock prices even before the actual policy change occurs (Kuttner, 2001).
Key References:
Amihud, Y., & Mendelson, H. (1986). "Asset Pricing and the Bid-Ask Spread." Journal of Financial Economics, 17(2), 223-249.
Fama, E. F., & French, K. R. (1992). "The Cross-Section of Expected Stock Returns." Journal of Finance, 47(2), 427-465.
Chordia, T., Roll, R., & Subrahmanyam, A. (2000). "Market Liquidity and Trading Activity." Journal of Finance, 55(2), 265-289.
Brunnermeier, M. K., & Pedersen, L. H. (2009). "Market Liquidity and Funding Liquidity." Review of Financial Studies, 22(6), 2201-2238.
Bernanke, B. S., & Gertler, M. (1999). "Monetary Policy and Asset Prices." NBER Working Paper No. 7559.
Kuttner, K. N. (2001). "Monetary Policy Surprises and Interest Rates: Evidence from the Fed Funds Futures Market." Journal of Monetary Economics, 47(3), 523-544.
These studies collectively highlight how liquidity influences stock market returns and how changes in liquidity conditions, influenced by monetary policy and other factors, can significantly impact stock prices and market stability.
S&P 2024: Magnificent 7 vs. the rest of S&PThis chart is designed to calculate and display the percentage change of the Magnificent 7 (M7) stocks and the S&P 500 excluding the M7 (Ex-M7) from the beginning of 2024 to the most recent data point. The Magnificent 7 consists of seven major technology stocks: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Alphabet (GOOGL), Meta (META), Nvidia (NVDA), and Tesla (TSLA). These stocks are a significant part of the S&P 500 and can have a substantial impact on its overall performance.
Key Components and Functionality:
1. Start of 2024 Baseline:
- The script identifies the closing prices of the S&P 500 and each of the Magnificent 7 stocks on the first trading day of 2024. These values serve as the baseline for calculating percentage changes.
2. Current Value Calculation:
- It then fetches the most recent closing prices of these stocks and the S&P 500 index to calculate their current values.
3. Percentage Change Calculation:
- The script calculates the percentage change for the M7 by comparing the sum of the current prices of the M7 stocks to their combined value at the start of 2024.
- Similarly, it calculates the percentage change for the Ex-M7 by comparing the current value of the S&P 500 excluding the M7 to its value at the start of 2024.
4. Plotting:
- The calculated percentage changes are plotted on the chart, with the M7’s percentage change shown in red and the Ex-M7’s percentage change shown in blue.
Use Case:
This indicator is particularly useful for investors and analysts who want to understand how much the performance of the S&P 500 in 2024 is driven by the Magnificent 7 stocks compared to the rest of the index. By showing the percentage change from the start of the year, it provides clear insights into the relative growth or decline of these two segments of the market over the course of the year.
Visualization:
- Red Line (M7 % Change): Displays the percentage change of the combined value of the Magnificent 7 stocks since the start of 2024.
- Blue Line (Ex-M7 % Change): Displays the percentage change of the S&P 500 excluding the Magnificent 7 since the start of 2024.
This script enables a straightforward comparison of the performance of the M7 and Ex-M7, highlighting which segment is contributing more to the overall movement of the S&P 500 in 2024.
3-Criteria StrategyThe "3-Criteria Strategy" is a simple yet effective trading strategy based on three criteria:
200-Day Moving Average: The first criterion checks whether the current price is above or below the 200-day moving average (SMA). A price above the 200-day line is considered bullish (thumbs up), while a price below is considered bearish (thumbs down).
5-Day Indicator: The second criterion evaluates the performance of the first five trading days of the year. If the closing price on the fifth trading day is higher than the closing price on the last trading day of the previous year, this is considered bullish (thumbs up). Otherwise, it's bearish (thumbs down).
Year-to-Date (YTD) Effect: The third criterion compares the current price with the closing price at the end of the previous year. A current price above the year-end price is bullish (thumbs up), while a price below is bearish (thumbs down).
Signal Interpretation:
Buy Signal: At least two of the three criteria must give a bullish signal (thumbs up).
Sell Signal: Zero or one bullish signal results in a bearish outlook.
The script provides visual cues with background colors:
Green background: Indicates a buy signal.
Red background: Indicates a sell signal.
Additionally, the script plots the 200-day moving average and the YTD line on the chart for better visualization.
Usage:
Apply the Script: Add the script to your TradingView chart.
Interpret Signals: Monitor the background color and the status label to determine trading actions.
Visual Aids: Use the 200-day line and YTD line plotted on the chart to confirm the criteria visually.
Scientific Research
The concepts used in this script—like the 200-day moving average and Year-to-Date effects—are well-documented in financial literature. However, the combination of these specific criteria as a trading strategy is more of a heuristic approach commonly used by traders rather than a subject of extensive academic research.
200-Day Moving Average: The 200-day moving average is widely regarded as a significant level in technical analysis, often serving as a demarcation between long-term bullish and bearish trends. Research has shown that long-term moving averages can be useful for trend-following strategies.
Reference: Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. Journal of Finance, 47(5), 1731-1764.
Year-to-Date and Calendar Effects: The Year-to-Date effect and early-year performance (such as the January effect) have been studied extensively in the context of seasonal market anomalies.
Reference: Rozeff, M. S., & Kinney, W. R. (1976). Capital Market Seasonality: The Case of Stock Returns. Journal of Financial Economics, 3(4), 379-402.
While these papers don't address the exact combination of criteria used in your strategy, they provide a solid foundation for understanding the underlying concepts.
