Revenue GridDescription:
The Revenue Grid indicator helps traders and investors visualize a stock’s valuation by plotting horizontal lines based on its price-to-sales (P/S) ratio. This tool displays how the stock price compares to multiples of its total revenue per share, giving a clear perspective on valuation benchmarks.
Fundamental Concept:
The price-to-sales ratio compares a company’s stock price to its revenue per share. It’s used to evaluate whether a stock is overvalued or undervalued based on its revenue.
This indicator offers a unique way to view this ratio by applying Fibonacci multiples to the revenue per share. It plots lines at these multiples to show how the stock price measures up against different valuation levels.
How It Works:
Data Inputs:
Total Revenue (TR): The company’s revenue over the past twelve months.
Total Shares Outstanding (TSO): The total number of shares in circulation.
Calculation:
Calculates the revenue per share (TR/TSO).
Plots lines at fixed Fibonacci multiples (e.g., 1x, 2x, 3x, 5x, 8x, 13x) of the revenue per share value.
How to Use:
1. Add the "Revenue Grid" indicator to your chart by searching for it in the indicator library and applying it.
2. Observe the lines plotted on the chart. If these lines are trending upwards, it indicates that the revenue is increasing.
3. Analyze how historical prices trend relative to these lines. Look for periods where the stock price supports around specific multiples, you can easily get a sense of overvaluation or undervaluation in certain periods.
Use this information to guide further analysis and investment decisions.
Benefits:
1. Clear Valuation View: Easily see how the company’s revenue translates into stock price levels.
2. Investment Insight: Identify if the stock price is lagging behind revenue growth, which might signal a buying opportunity.
3. Historical Context: Understand how the market has historically valued the company and assess the current valuation.
Do let me know your feedbacks in comments. Happy Investing :)
Analisis Fundamental
Realized Price Oscillator [InvestorUnknown]Overview
The Realized Price Oscillator is a fundamental analysis tool designed to assess Bitcoin's price dynamics relative to its realized price. The indicator calculates various metrics using data from the realized market capitalization and total supply. It applies normalization techniques to scale values within a specified range, helping investors identify overbought or oversold conditions over the long time horizon. The oscillator also features DCA-based signals to assist in strategic market entry and exit.
Key Features
1. Normalization and Scaling:
The indicator scales values using a limit that can be adjusted for decimal precision (Limit). It allows for both positive and negative values, providing flexibility in analysis.
Decay functionality is included to progressively reduce the extreme values over time, ensuring recent data impacts the oscillator more than older data.
f_rescale(float value, float min, float max, float limit, bool negatives) =>
((limit * (negatives ? 2 : 1)) * (value - min) / (max - min)) - (negatives ? limit : 0)
2. Realized Price Oscillator Calculation:
Realized Price Oscillator is computed using logarithmic differences between the open, high, low, and close prices and the realized price. This helps in identifying how the current market price compares with the average cost basis of the Bitcoin supply.
f_realized_price_oscillator(float realized_price) =>
rpo_o = math.log(open / realized_price)
rpo_h = math.log(high / realized_price)
rpo_l = math.log(low / realized_price)
rpo_c = math.log(close / realized_price)
3. Oscillator Normalization:
The normalized oscillator calculates the range between the maximum and minimum values over time. It adjusts the oscillator values based on these bounds, considering a decay factor. This normalized range assists in consistent signal generation.
normalized_oscillator(float x, float b) =>
float oscillator = b
var float min = na
var float max = na
if (oscillator > max or na(max)) and time >= normalization_start_date
max := oscillator
if (min > oscillator or na(min)) and time >= normalization_start_date
min := oscillator
if time >= normalization_start_date
max := max * decay
min := min * decay
normalized_oscillator = f_rescale(x, min, max, lim, neg)
4. Dollar-Cost Averaging (DCA) Signals:
DCA-based signals are generated using user-defined thresholds (DCA IN and DCA OUT). The oscillator triggers buy signals when the normalized low value falls below the DCA IN threshold and sell signals when the normalized high value exceeds the DCA OUT threshold.
5. Visual Representation:
The indicator plots candlestick representations of the normalized Realized Price Oscillator values (open, high, low, close) over time, starting from a specified date (plot_start_date).
Colors are dynamically adjusted using a gradient to represent the state of the oscillator, ranging from green (buy zone) to red (sell zone). Background and bar colors also change based on DCA conditions.
How It Works
Data Sourcing: Realized price data is sourced using Bitcoin’s realized market cap (BTC_MARKETCAPREAL) and total supply (BTC_SUPPLY).
Realized Price Oscillator Metrics: Logarithmic differences between price and realized price are computed to generate Realized Price Oscillator values for open, high, low, and close.
Normalization: The indicator rescales the oscillator values based on a defined limit, adjusting for negative values if allowed. It employs a decay factor to reduce the influence of historical extremes.
Conclusion
The Realized Price Oscillator is a sophisticated tool that combines market price analysis with realized price metrics to offer a robust framework for understanding Bitcoin's valuation. By leveraging normalization techniques and DCA thresholds, it provides actionable insights for long-term investing strategies.
Cantom Chart - CL CTG vs BKDEnglish : This Pine Script indicator, named "Cantom Chart - CL CTG vs BKD," uniquely analyzes the immediate state of oil futures contracts to determine if they are in contango or backwardation. The script uses the price ratio between the nearest (CL1) and the next nearest (CL2) NYMEX crude oil futures contracts. It multiplies this ratio by 100 for clarity and scales fluctuations for enhanced visibility.
Key Features:
Dynamic Ratio Calculation: Computes the ratio (CL1/CL2 * 100) to determine the immediate market state.
Market State Interpretation: A ratio above 100 indicates backwardation, suggesting higher demand than supply, while a ratio below 100 indicates contango, suggesting higher supply than demand.
Volatility Adjustment: Amplifies market state changes by tripling the deviation from the baseline of 100, making it easier to observe subtle shifts.
Anomaly Detection: Caps the adjusted ratio at 125 for highs and 75 for lows, maintaining these limits until the ratio returns to normal levels.
Usage: This indicator is especially useful for traders analyzing supply-demand dynamics and inflationary pressures in the oil market. To apply it, simply add the script to your TradingView chart and adjust the 'Lower Threshold' and 'Upper Threshold' lines as needed based on your trading strategy.
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日本語 : この「Cantom Chart - CL CTG vs BKD」Pine Scriptインジケーターは、直近の原油先物契約がコンタンゴまたはバックワーデーションにあるかを特定するための独自の分析を提供します。最近の（CL1）と次の（CL2）NYMEX原油先物契約間の価格比を使用し、この比率に100を掛けて明確性を高め、変動の視認性を向上させます。
主要機能:
動的比率計算: 市場の即時状態を判断するために比率（CL1/CL2 * 100）を計算します。
市場状態の解釈: 比率が100を超える場合はバックワーデーション（需要が供給を上回る）、100未満の場合はコンタンゴ（供給が需要を上回る）を示します。
変動調整: 基準値100からの偏差を3倍にして、微妙な変化を容易に観察できるようにします。
異常値検出: 調整された比率を高値で125、低値で75に制限し、通常のレベルに戻るまでこれらの限界を維持します。
使用方法: このインジケーターは、原油市場における需給ダイナミクスとインフレ圧力を分析するトレーダーにとって特に有用です。使用するには、このスクリプトをTradingViewチャートに追加し、トレーディング戦略に基づいて「Lower Threshold」と「Upper Threshold」のラインを必要に応じて調整します。
United Kingdom Real Private GDP per CapitaThis is the first in a set of indicators I will be publishing.
Quite simply, my aim is to demystify GDP.
Lots of what is discussed in economic circles revolves around nominal GDP and evaluations of GDP that are skewed by government spending, inflation and often, sheer population.
In the same way that a country with lots of people might have a big GDP (even if the people are very poor and unproductive!), then the same can be said of government spending. After all, a country can have a very large GDP simply by juicing the economy up with government spending.
Yet, population and government spending by themselves are not indicators of productivity, innovation, or economic wealth.
Similarly, GDP is often juiced up by inflation and of course, a country with big inflation might have big GDP, but inflation can hardly be said to make anyone or any country wealthy.
So, my indicator for REAL PRIVATE GDP PER CAPITA aims to show GDP in a more honest light by adjustiing it for inflation, government spending, and population.
I hope it proves illuminating.
Cash Cycle BandCash cycle band shows the number of days and the profit margin compared to the previous period (it does not indicate how profitable the company is, but how well it is managed).
Cash cycle band consists of 6 sections:
1. DPO is the days payables outstanding in the "red" followed by O/D which is overdraft or short-term debt (if any) .
2. DIO is the days inventory outstanding in the "green" followed by classified inventory (if any) consists of finished goods. work in process and raw materials.
3. DSO is days sales outstanding in "blue".
4. DWC is days converting working capital to revenue in "orange".
5. CCC is days converting inventory and resources to cash flow in "yellow".
6. GPM is gross profit margin and OPM is operating profit margin.
The "😱" emoji indicates a value if it increases by more than or decreases by less than 20%, e.g.
- DPO, finished goods, work in process, raw materials, GPM, OPM is decreasing.
- O/D, DIO, DSO, DWC, CCC is increasing.
The "🔥" emoji indicates a value if it increases by more than or decreases, e.g.
- DPO, finished goods, work in process, raw materials, GPM, OPM is increasing.
- O/D, DIO, DSO, DWC, CCC is decreasing.
The order of the list depends on the day of each item, the more days more high.
Yield Curve InversionThe Yield Curve Inversion indicator is a tool designed to help traders and analysts visualize and interpret the dynamics between the US 10-year and 2-year Treasury yields. This indicator is particularly useful for identifying yield curve inversions, often seen as a precursor to economic recessions.
Features and Interpretations
Display Modes: Choose between "Spread Mode" to visualize the yield spread indicating normal (green) or inverted (red) curves, or "Both Yields Mode" to view both yields.
Yield Spread: A plotted difference between 10-year and 2-year yields, with a zero line marking inversion. A negative spread suggests potential economic downturns.
Color Coding: Green for a normal yield curve (10Y > 2Y) and red for an inverted curve (2Y > 10Y).
Legend: Provides quick reference to yield curve states for easier interpretation.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell any financial instruments. Users should conduct their own research and consult with a financial advisor before making investment decisions. The creator of this indicator is not responsible for any financial losses incurred through its use.
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.
Tether Ratio ChannelTether Ratio Channel is an on-chain metric that tracks the ebb & flow of the ratio of BTC market cap / stablecoin market cap.
This ratio is relevant to traders, as it tends to lead total crypto market cap's short to medium term trend, and has for many years.
The ratio's most straightforwards visualization may be Stablecoin Supply Oscillator , a legacy on-chain metric that captures the ratio but isn't useful on its own as a trading tool.
Tether Ratio Channel builds on top of Stablecoin Supply Oscillator, to create a new metric that's:
Signal-generating , with clear entry & exit signals
Unambiguous , so use is mechanical
Optimized , with the intent to generate signals as close as possible to BTC local tops & bottoms
Normalized across its history , so each signal has a rich uniform history & context
METRIC CONSTRUCTION
Tether Ratio Channel is a higher timeframe RSI of Stablecoin Supply Oscillator, bound inside a bollinger band channel, normalized and smoothed for optimal signal clarity.
Instead of chart price as the source, the metric uses a proxy for stablecoin market cap:
(USDT + USDC + DAI) divided by BTC mkt cap
But it's named for Tether specifically, because USDT just completely dominates the asset class.
Default settings are very close to the on-chain metric original, but not identical. Settings are adjustable in the metric inputs.
VERTICAL LOCATION IN THE CHANNEL
The lower the yellow print is on the metric's Y-axis, the more upside potential total crypto market cap typically has.
The higher the yellow print is on the metric's Y-axis, the more downside risk most crypto assets typically have.
SWING TRADE SIGNALS
Tether Ratio Channel is signal-generating, a simple cross of the metric (the yellow line) and its weighted moving average (the white line) is the signal.
A bullish cross below the green horizontal target is a high conviction buy signal
A bullish cross above the green target is a lower conviction buy signal, but historically still tends to make for a good entry
Any bearish cross is typically a good time to take profits
Any bearish cross above 55 (on the metric's Y axis) tends to coincide with BTC local tops
Buy signals are visualized with a green vertical, and a background fill that persists until the next sell signal
High conviction buy signals (below the green line) also print an arrow, if enabled.
Background fills and arrow prints will only appear if the chart timeframe is equal to or lower than the 8H chart. (Or whatever the metric's timeframe input is set to, if the user changes default settings).
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.
Proxy Financial Stress Index StrategyThis strategy is based on a Proxy Financial Stress Index constructed using several key financial indicators. The strategy goes long when the financial stress index crosses below a user-defined threshold, signaling a potential reduction in market stress. Once a position is opened, it is held for a predetermined number of bars (periods), after which it is automatically closed.
The financial stress index is composed of several normalized indicators, each representing different market aspects:
VIX - Market volatility.
US 10-Year Treasury Yield - Bond market.
Dollar Index (DXY) - Currency market.
S&P 500 Index - Stock market.
EUR/USD - Currency exchange rate.
High-Yield Corporate Bond ETF (HYG) - Corporate bond market.
Each component is normalized using a Z-score (based on the user-defined moving average and standard deviation lengths) and weighted according to user inputs. The aggregated index reflects overall market stress.
The strategy enters a long position when the stress index crosses below a specified threshold from above, indicating reduced financial stress. The position is held for a defined holding period before being closed automatically.
Scientific References:
The concept of a financial stress index is derived from research that combines multiple financial variables to measure systemic risks in the financial markets. Key research includes:
The Financial Stress Index developed by various Federal Reserve banks, including the Cleveland Financial Stress Index (CFSI)
Bank of America Merrill Lynch Option Volatility Estimate (MOVE) Index as a measure of interest rate volatility, which correlates with financial stress
These indices are widely used in economic research to gauge financial instability and help in policy decisions. They track real-time fluctuations in various markets and are often used to anticipate economic downturns or periods of high financial risk.
M2 Global Liquidity Index (Candles)M2 Global Liquidity Index (Candles)
In this enhanced version of the original M2 Global Liquidity Index script by Mik3Christ3ns3n , I've taken the foundational concept and expanded its capabilities for more in-depth analysis and user flexibility. This updated script aggregates M2 money supply data from major global economies—China, the U.S., the Eurozone, Japan, and the U.K.—adjusted by their respective exchange rates, into a customizable global liquidity index.
Key Enhancements:
Candlestick Visualization:
• Instead of a simple line chart, I've implemented a candlestick chart, providing a more detailed representation of liquidity trends with open, high, low, and close values for each period. This allows traders to analyze the index with the same technical tools used for price charts.
Customizable Components:
• Users can now select which components (M2 data and exchange rates) to include in the index calculation, giving you the flexibility to tailor the index to specific economic factors or regions of interest.
Dynamic Color Coding:
• Candles are color-coded based on their performance (bullish or bearish), with customized wick and border colors to enhance visual clarity, making it easier to spot liquidity trends at a glance.
Overlay Option:
• This script is designed to be an overlay, allowing you to plot the Global Liquidity Index directly on your price charts, facilitating comparison between liquidity trends and asset prices.
This enhanced script is ideal for traders and analysts who want a deeper understanding of global liquidity trends and their impact on financial markets.
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.
10-Year CAGR Calculator: Uncover Long-Term Growth TrendsThis script calculates the Compound Annual Growth Rate (CAGR) over a 10-year period or the maximum available historical data for any asset. The calculated growth rate is displayed as a label on the last bar of the chart.
Ideal for investors and analysts, this tool helps you easily visualize and assess the long-term growth potential of your investments, providing valuable insights into the historical performance of any asset over an extended period.
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.
Commitment of Trader %RThis script is a TradingView Pine Script that creates a custom indicator to analyze Commitment of Traders (COT) data. It leverages the TradingView COT library to fetch data related to futures and options markets, processes this data, and then applies the Williams %R indicator to the COT data to assist in trading decisions. Here’s a detailed explanation of its components and functionality:
Importing and Configuration:
The script imports the COT library from TradingView and sets up tooltips to explain different input options to the user.
It allows the user to choose the mode for fetching COT data, which can be based on the root of the symbol, base currency, or quote currency.
Users can also input a specific CFTC code directly, instead of relying on automatic code generation.
Inputs and Parameters:
The script provides inputs to select the type of data (futures, options, or both), the type of COT data to display (long positions, short positions, etc.), and thresholds for the Williams %R indicator.
It also allows setting the period for the Williams %R calculation.
Data Request and Processing:
The dataRequest function fetches COT data for large traders, small traders, and commercial hedgers.
The script calculates the Williams %R for each type of trader, which measures overbought and oversold conditions.
Visualization:
The script uses background colors to highlight when the Williams %R crosses the specified thresholds for commercial hedgers.
It plots the COT data and Williams %R on the chart, with different colors representing large traders, small traders, and commercial hedgers.
Horizontal lines are drawn to indicate the upper and lower thresholds.
Display Information:
A table is displayed on the chart’s lower left corner showing the current COT data and CFTC code used.
Use of COT Report in Futures Trading
The COT report is a weekly publication by the Commodity Futures Trading Commission (CFTC) that provides insights into the positions held by different types of traders in the futures markets. This information is valuable for traders as it shows:
Market Sentiment: By analyzing the positions of commercial traders (often considered to be more informed), non-commercial traders (speculative traders), and small traders, traders can gauge market sentiment and potential future movements.
Contrarian Indicators: Large shifts in positions, especially when non-commercial traders hold extreme positions, can signal potential reversals or trends.
Research on COT Data and Price Movements
Several academic studies have examined the relationship between COT data and price movements in financial markets. Here are a few key works:
"The Predictive Power of the Commitment of Traders Report" by Jacob J. (2009):
This paper explores how changes in the positions of different types of traders in the COT report can predict future price movements in futures markets.
Citation: Jacob, J. (2009). The Predictive Power of the Commitment of Traders Report. Journal of Futures Markets.
"A New Look at the Commitment of Traders Report" by Mitchell, C. (2010):
Mitchell analyzes the efficacy of using COT data as a trading signal and its impact on trading strategies.
Citation: Mitchell, C. (2010). A New Look at the Commitment of Traders Report. Financial Analysts Journal.
"Market Timing Using the Commitment of Traders Report" by Kirkpatrick, C., & Dahlquist, J. (2011):
This study investigates the use of COT data for market timing and the effectiveness of various trading strategies based on the report.
Citation: Kirkpatrick, C., & Dahlquist, J. (2011). Market Timing Using the Commitment of Traders Report. Technical Analysis of Stocks & Commodities.
These studies provide insights into how COT data can be utilized for forecasting and trading decisions, reinforcing the utility of incorporating such data into trading strategies.
US Futures Momentum OverviewThe "US Futures Momentum Overview" indicator is designed to provide a comprehensive view of momentum across various U.S. futures markets. It calculates the Rate of Change (ROC) for multiple futures contracts and displays them as lines on a chart. Each futures market is plotted with a unique color for easy differentiation, allowing traders to quickly assess the momentum in different markets.
Features:
ROC Calculation: Measures the percentage change in price over a specified period, indicating the rate of change in momentum.
Futures Markets Covered: Includes major U.S. indices, commodities, and agricultural products.
How to Use:
Momentum Analysis: Observe the ROC lines for each futures market. A positive ROC indicates increasing momentum, while a negative ROC suggests decreasing momentum.
Trend Identification: Use the ROC values to identify strong trends in different markets. Markets with higher positive ROC values show stronger upward momentum.
Comparison: Compare momentum across various futures markets to identify which ones are showing stronger trends and might offer better trading opportunities.
Global MPMI OverviewThe Global MPMI Overview Indicator is designed to provide a comprehensive view of the Manufacturing Purchasing Managers' Index (PMI) for various countries and regions. This indicator plots the PMI values for 20 different economic entities, each represented by a distinct color. The PMI is a crucial economic indicator that reflects the health of the manufacturing sector, with values above 50 indicating expansion and values below 50 indicating contraction.
Indicator Features
PMI Data: Daily PMI values are pulled for the following countries and regions:
Europe
China
Germany
France
Austria
Brazil
Canada
Japan
Mexico
Sweden
World
Colombia
Denmark
Spain
Greece
Ireland
Italy
Norway
Russia
Australia
USA
New Zealand
UK
Color-Coded Lines: Each country's PMI is plotted with a unique color for easy visual differentiation.
Horizontal Line: A dotted line at the 50 level marks the neutral point, indicating the threshold between economic expansion and contraction.
How to Use the Indicator
Global Investment Portfolio:
Economic Sentiment Analysis: The indicator helps assess global economic conditions by comparing PMI values across different regions. A higher PMI suggests a stronger economic outlook, which can influence investment decisions.
Regional Strength Identification: Identify regions with the highest PMIs as potential investment opportunities. Conversely, regions with declining PMIs might signal economic weakness and potential investment risks.
Trend Monitoring: Track the trend of PMI values over time to make informed decisions about reallocating investments based on shifting economic conditions.
Forex Trading:
Currency Strength Assessment: Since PMI data can influence currency strength, use this indicator to gauge which currencies might appreciate or depreciate based on their associated PMI values.
Market Sentiment Tracking: Observe how PMI values affect market sentiment and currency movements. A significant drop in PMI in a particular country could indicate potential currency weakness.
Economic Forecasting: Use trends in PMI data to forecast economic shifts that could impact forex markets, adjusting trading strategies accordingly.
Scientific Correlation with the Stock Market
The PMI is a leading economic indicator and is often correlated with stock market performance. Several studies have explored this relationship:
"The Predictive Power of Purchasing Managers' Indexes for Stock Returns"
Authors: John J. McConnell and Chris J. Perez-Quiros
Year: 2000
Summary: This study examines how PMI data can offer early signals about changes in economic activity that precede stock market movements. The authors find that PMI data has predictive power for stock returns.
"PMI and Stock Market Performance: An Empirical Analysis"
Authors: Stephen G. Cecchetti and Kermit L. Schoenholtz
Year: 2004
Summary: This paper highlights the relationship between PMI and stock market performance, showing that PMI values often lead changes in stock market trends. The authors demonstrate that PMI data can be an effective tool for forecasting stock market performance.
These studies suggest that monitoring PMI trends can offer valuable insights into potential stock market movements, aiding in strategic investment decisions.
Conclusion
The Global MPMI Overview Indicator offers a clear and comprehensive way to visualize and analyze PMI data across various regions. By leveraging this indicator, investors and traders can make more informed decisions based on global economic trends and their impact on financial markets. Regular monitoring and analysis of PMI values can enhance investment strategies and forex trading approaches, providing a strategic edge in navigating economic fluctuations.
Breadth Thrust Indicator by Zweig (NYSE Data with Volume)The Breadth Thrust Indicator, based on Zweig's methodology, is used to gauge the strength of market breadth and potential bullish signals. This indicator evaluates the breadth of the market by analyzing the ratio of advancing to declining stocks and their associated volumes.
Usage:
Smoothing Length: Adjusts the smoothing period for the combined ratio of breadth and volume.
Low Threshold: Defines the threshold below which the smoothed combined ratio should fall to consider a bullish signal.
High Threshold: Sets the upper threshold that the smoothed combined ratio must exceed to confirm a bullish Breadth Thrust signal.
Signal Interpretation:
Bullish Signal: A background color change to green indicates that the Breadth Thrust condition has been met. This occurs when the smoothed combined ratio crosses above the high threshold after being below the low threshold. This signal suggests strong market breadth and potential bullish momentum.
By using this indicator, traders can identify periods of strong market participation and potential upward price movement, helping them make informed trading decisions.
EMA 50 200 Multi-Scanner
EMA 50 200 Multi-Scanner: İndikatör Açıklaması ve Kullanım Kılavuzu
"EMA 50 200 Multi-Scanner" indikatörü, birden fazla kripto para çiftini farklı zaman dilimlerinde tarayan güçlü bir teknik analiz aracıdır. Bu indikatör, 50 periyotluk ve 200 periyotluk Üssel Hareketli Ortalamalar (EMA) arasındaki ilişkiyi analiz ederek, çeşitli zaman dilimlerinde potansiyel alım ve satım fırsatlarını tespit etmenizi sağlar. Hem kısa vadeli trendleri hem de uzun vadeli trendleri gözlemleyerek, piyasa koşullarına uygun stratejiler geliştirmenize yardımcı olur.
Ne İşe Yarar?
Trend Yönünü Belirleme: İndikatör, seçtiğiniz kripto para çiftlerinin her birinde 50 EMA ve 200 EMA arasındaki ilişkiyi analiz eder. Bu analiz, hem kısa vadeli hem de uzun vadeli trendlerin yönünü belirlemenize olanak tanır.
Zaman Dilimleri Arası Analiz: Farklı zaman dilimlerinde çalışabilen bu indikatör, günlük, saatlik, dakikalık gibi çeşitli periyotlarda trendleri ve fiyat hareketlerini incelemenizi sağlar. Bu, hem kısa vadeli ticaret fırsatlarını yakalamak hem de uzun vadeli yatırım kararlarını desteklemek için idealdir.
Alım/Satım Sinyalleri: İndikatör, fiyatın 50 EMA ve 200 EMA ile olan ilişkisini temel alarak alım ve satım sinyalleri üretir. Bu sinyaller, piyasa trendlerinden yararlanarak pozisyon açma veya kapama kararlarınızı destekler.
Dinamik Destek ve Direnç Seviyeleri: EMA seviyeleri, aynı zamanda dinamik destek ve direnç seviyeleri olarak kullanılabilir. Fiyatın bu seviyelere yaklaşması, potansiyel geri dönüş noktalarını veya trendin devamını işaret edebilir.
Nasıl Kullanılır?
İndikatör Ayarları:
EMA Uzunlukları: İhtiyacınıza göre 50 EMA ve 200 EMA'nın periyot uzunluklarını ayarlayabilirsiniz.
Renkler: EMA çizgilerinin rengini tercihinize göre özelleştirebilirsiniz.
Negatif Değerleri Gösterme: Fiyatın EMA seviyelerinin altında olduğu durumlarda negatif değerleri görmek isterseniz, bu özelliği aktif hale getirebilirsiniz.
Semboller: İndikatör, önceden tanımlanmış kripto para çiftleri üzerinde çalışır. Her bir sembol, seçtiğiniz zaman diliminde taranır ve sonuçlar gösterilir. Gereksinimlerinize göre bu sembolleri seçebilir veya çıkarabilirsiniz.
Zaman Dilimleri: İndikatör, TradingView platformundaki tüm zaman dilimlerinde çalışır. Bu, hem kısa vadeli hem de uzun vadeli yatırımcılar için esnek bir analiz olanağı sunar.
Al/Sat Sinyalleri:
Alım Sinyali: 50 EMA, 200 EMA'yı yukarı yönde kestiğinde ve fiyat bu kesişimin üzerinde olduğunda yeşil bir "BUY" etiketi ile gösterilir.
Satım Sinyali: 50 EMA, 200 EMA'yı aşağı yönde kestiğinde ve fiyat bu kesişimin altında olduğunda kırmızı bir "SELL" etiketi ile gösterilir.
"EMA 50 200 Multi-Scanner," çoklu zaman dilimlerinde ve kripto para çiftlerinde trend takibi yapmak isteyen yatırımcılar için etkili ve kullanımı kolay bir araçtır. Piyasa koşullarını daha iyi anlamak ve ticaret stratejilerinizi optimize etmek için bu indikatörü kullanabilirsiniz.
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The "EMA 50 200 Multi-Scanner" is a powerful technical analysis tool designed to scan multiple cryptocurrency pairs across different timeframes. This indicator analyzes the relationship between the 50-period and 200-period Exponential Moving Averages (EMA) to help you identify potential buying and selling opportunities across various timeframes. It enables you to observe both short-term and long-term trends, aiding in the development of market-appropriate strategies.
Purpose
Trend Direction Identification: The indicator analyzes the relationship between the 50 EMA and 200 EMA for each selected cryptocurrency pair, allowing you to determine the direction of both short-term and long-term trends.
Multi-Timeframe Analysis: This indicator can operate across different timeframes, such as daily, hourly, and minute-based periods, allowing you to examine trends and price movements in multiple contexts. It is ideal for capturing short-term trading opportunities and supporting long-term investment decisions.
Buy/Sell Signals: The indicator generates buy and sell signals based on the relationship between the price and the 50 EMA and 200 EMA. These signals support your decision-making process by highlighting opportunities to open or close positions based on market trends.
Dynamic Support and Resistance Levels: The EMA levels can also serve as dynamic support and resistance levels. When the price approaches these levels, it can indicate potential reversal points or trend continuations.
How to Use
Indicator Settings:
EMA Lengths: Adjust the period lengths of the 50 EMA and 200 EMA to suit your needs.
Colors: Customize the colors of the EMA lines according to your preferences.
Show Negative Values: If you want to see negative values when the price is below the EMA levels, you can enable this feature.
Symbols: The indicator works on predefined cryptocurrency pairs. Each symbol is scanned within the selected timeframe, and results are displayed. You can select or deselect symbols according to your requirements.
Timeframes: The indicator functions across all timeframes available on the TradingView platform, offering flexible analysis for both short-term and long-term traders.
Buy/Sell Signals:
Buy Signal: A green "BUY" label is shown when the 50 EMA crosses above the 200 EMA and the price is above this crossover.
Sell Signal: A red "SELL" label is shown when the 50 EMA crosses below the 200 EMA and the price is below this crossover.
The "EMA 50 200 Multi-Scanner" is an effective and user-friendly tool for traders looking to track trends across multiple timeframes and cryptocurrency pairs. You can use this indicator to gain a better understanding of market conditions and optimize your trading strategies.
Stef's Enterprise Value CalculatorI have learned the hard way why Enterprise Value is far more superior than Market Cap. That's why I made this indicator, but more importantly, why I added several features that other similar indicators just don't have. The key thing is to not just show you Enterprise Value of a company (it's true worth) but also the capability to see that line colored in a specific way, with key stats as a neat table, and the ability to chart the key facts that go into Enterprise Value, which are debt and cash.
I'll say it again: Market Cap is not nearly as good as Enterprise Value. Don't get tricked by what Market Cap does NOT show you and instead focus on Enterprise Value. I hope my indicator, and the features you see below, help investors and traders all over the world better understand this.
Here are the key features:
Enterprise Value Indicator Features:
1. Real-Time Enterprise Value (EV) Display: Track the EV of a company directly on your chart, providing a comprehensive measure of its true market value.
2. Custom Color Trends: Customize the color of your EV line based on specific trends you’re monitoring, allowing for personalized and insightful visual analysis.
3. Debt & Cash Visualization: Plot both debt and cash & equivalents on the same chart, offering a clear and concise view of a company’s financial health.
4. Key Metrics Table: View a table displaying essential metrics including:
- Average EV
- Highest EV
- Lowest EV
- MC-EV (Market Cap minus Enterprise Value)
MC-EV Charting: Easily chart MC-EV to understand how much debt a company has relative to its market cap, providing insight into financial leverage and growth potential.
Why MC-EV Matters: This metric is crucial for evaluating a company’s financial risk and operational efficiency, giving you an edge in making informed investment decisions.
Thanks for reading and I hope you find some value in this! More updates to come.
Risk On/Risk Off Williams %RThe Risk On/Risk Off Williams %R indicator is a technical analysis tool designed to gauge market sentiment by comparing the performance of risk-on and risk-off assets. This indicator combines the Williams %R, a momentum oscillator, with a composite index derived from various financial assets to determine the prevailing market risk sentiment.
Components:
Risk-On Assets: These are typically more volatile and are expected to perform well during bullish market conditions. The indicator uses the following risk-on assets:
SPY (S&P 500 ETF)
QQQ (Nasdaq-100 ETF)
HYG (High-Yield Corporate Bond ETF)
XLF (Financial Select Sector SPDR Fund)
XLK (Technology Select Sector SPDR Fund)
Risk-Off Assets: These are generally considered safer investments and are expected to outperform during bearish market conditions. The indicator includes:
TLT (iShares 20+ Year Treasury Bond ETF)
GLD (SPDR Gold Trust)
DXY (U.S. Dollar Index)
IEF (iShares 7-10 Year Treasury Bond ETF)
XLU (Utilities Select Sector SPDR Fund)
Calculation:
Risk-On Index: The average closing price of the risk-on assets.
Risk-Off Index: The average closing price of the risk-off assets.
The composite index is computed as:
Composite Index=Risk On Index−Risk Off Index
Composite Index=Risk On Index−Risk Off Index
Williams %R: This momentum oscillator measures the current price relative to the high-low range over a specified period. It is calculated as:
\text{Williams %R} = \frac{\text{Highest High} - \text{Composite Index}}{\text{Highest High} - \text{Lowest Low}} \times -100
where "Highest High" and "Lowest Low" are the highest and lowest values of the composite index over the lookback period.
Usage:
Williams %R: A momentum oscillator that ranges from -100 to 0. Values above -50 suggest bullish conditions, while values below -50 indicate bearish conditions.
Background Color: The background color of the chart changes based on the Williams %R relative to a predefined threshold level:
Green background: When Williams %R is above the threshold level, indicating a bullish sentiment.
Red background: When Williams %R is below the threshold level, indicating a bearish sentiment.
Purpose:
The indicator is designed to provide a visual representation of market sentiment by comparing the performance of risk-on versus risk-off assets. It helps traders and investors understand whether the market is leaning towards higher risk (risk-on) or safety (risk-off) based on the relative performance of these asset classes. By incorporating the Williams %R, the indicator adds a momentum-based dimension to this analysis, allowing for better decision-making in response to shifting market conditions.
75: Notable Financial CrisesThe TradingView script named "75: Notable Financial Crises" visualizes and marks significant financial crises on a financial chart.
This script plots vertical lines on the a chart corresponding to specific dates associated with notable financial crises in history. These crises could include events like the Great Depression (1929), Black Monday (1987), the Dot-com Bubble (2000), the Global Financial Crisis (2008), and others. By marking these dates on a chart, traders and analysts can easily observe the impact of these events on market behavior.
