DTC AIO [US] Why this is one tool, not a bundle of indicators
A stock's chart alone cannot tell you whether it is a genuine market leader. A rising 50-day average looks the same whether the earnings behind it are accelerating or shrinking; a strong-looking breakout looks the same whether the whole sector is moving or just that one ticker. Answering "is this a leader worth trading" requires checking several unrelated data sources against each other at the same time — the company's actual earnings, its price behavior relative to the market, how it behaves specifically when the market is under stress, and how it stacks up against the handful of stocks that compete with it. None of those four checks alone is reliable; a stock can look strong on any one of them and still not be a real leader. This script exists because doing that cross-check by hand — pulling up earnings, then flipping to a relative-strength chart, then manually building a peer watchlist — is slow and easy to skip. It runs all four checks against the same symbol on the same chart, automatically, and only then hands you the price-structure tools (moving averages, an anchored VWAP, pattern markers) needed to time an entry once that leadership case is actually made. The scoring engines are the reason this script exists; the timing tools are there so you are not forced to add three more indicators once you have your answer.
The four leadership checks
- Earnings engine. Quarterly or annual earnings and sales are pulled from PulseWire's financial data and laid out in a MarketSmith-style grid: the primary metric (earnings per share, or net income if you prefer), its year-over-year percentage change, sales, and the sales percentage change, with optional gross-margin and return-on-equity rows. Year-over-year is measured against the same period one year earlier so seasonal businesses compare fairly. A year-over-year change measured off a negative prior-year base is flagged with a "#", the standard convention for marking a percentage that would otherwise be misleading (e.g. earnings improving from -$1.00 to -$0.10 is not really a "-90%" move).
- Relative strength versus the market. A relative-strength line is built by dividing the stock's price by a benchmark's price (SPY by default), then scaling that ratio so it plots alongside the stock's own price. A one-year percentile rank of that ratio produces a 1-99 "RS Rating" — this is the same underlying idea used by IBD's RS Rating (how a stock's performance ranks against the rest of the market over the past year), calculated independently here from price data rather than licensed from any provider.
- Relative strength during stress ("Panic RS"). This checks something the plain RS line does not: whether the stock is holding above its own short-term average on days when the benchmark itself is below its own short-term average — in other words, is this stock outperforming specifically while the broad market is under pressure. That is a materially different (and rarer) signal than simply outperforming during a rally, and it is flagged with its own marker.
- Burst score (volatility regime). Instead of a single volatility number like ATR, this counts how many days over a chosen lookback (3 months to 3 years) closed up 5%, 10% and 17% or more, then combines those three counts into one score. A stock that regularly produces large up-days behaves very differently from one that grinds slowly upward even if their average volatility looks similar, and that difference is often visible in this count before it shows up in a standard momentum indicator.
- Automatic peer comparison. The stock's industry (or sector, as a fallback) is matched against a built-in map of roughly 60 US industry groups, each with a curated list of representative peer tickers, and a comparison table is built automatically from that group — day, 1-month and 3-month return, relative volume, and an RS column for each peer, with the current symbol pinned at the top. The RS column ranks each peer's 3-month return against the OTHER peers actually shown in the table (a 0-100 scale, highest = strongest of the group) — a peer-group-relative read, deliberately not the same 1-year-vs-market calculation the main RS Rating uses, since ranking a handful of direct competitors against each other is the more useful comparison in a table built specifically to check group leadership. You are not expected to build or maintain your own watchlist of comparable stocks; the peer set is derived from the symbol you already have on the chart.
Timing tools (used once the leadership case is made, not standalone)
- Four configurable moving averages (simple, exponential or weighted; independent length, color and width) for the standard support/trend read.
- An anchored VWAP measured from the most recent all-time high forward, giving a volume-weighted "fair value" line for the current up-leg rather than an arbitrary fixed lookback.
- Average daily range percentage and relative volume, so a breakout can be judged against the stock's own normal range and normal volume rather than an absolute number.
- Pattern markers: inside bars, a simplified pocket-pivot flag (an up day of 5%+ on above-threshold volume), the lowest-volume day over a lookback (often precedes a move), "three weeks tight" closes (three consecutive weekly closes within a volatility-scaled band of each other, an IBD base-tightening pattern), and swing high/low pivot labels with optional percentage change between them.
Compact dashboard
A small, repositionable table (top-right by default) puts the numbers behind the leadership read in one place: RS Rating, relative volume, average daily range %, 3-month return, the burst score, and float %. A stretched average daily range (7% or more) or an already-extended 3-month return (80% or more) is flagged in red with a ⚠ marker as a "this has probably already moved a lot" caution. Market cap, free float, and average dollar volume are available as the earnings table's configurable top-left header cell instead of a separate panel, so they sit next to the earnings grid they help contextualize.
How to use it
1. Add it to a daily chart of a US stock.
2. Check the earnings grid and the RS line/rating first: you want rising year-over-year earnings and sales together with relative strength making new highs against the benchmark.
3. Check whether the Panic RS markers and burst score are present — that tells you whether the leadership is showing up specifically during market weakness, and whether the stock has the range profile of an actual leader rather than a slow grinder.
4. Check the peer table to confirm the stock is leading its own group, not just riding the index up.
5. Once those four checks line up, use the moving-average stack, the anchored VWAP and the pattern markers to time an entry near support, sizing with the daily-range and relative-volume readings.
6. Every block has its own on/off toggle, so the dashboard can be reduced to only the checks you personally use.
Notes
- Earnings, sales, margin, return on equity, and the market-cap/float figures come from PulseWire's financial data and are only as complete as that data is for a given symbol; missing values show a dash rather than a misleading zero.
- Defaults assume US equities on a daily timeframe with a broad-market benchmark; the script will run on other markets and timeframes, but those defaults are US-equity-specific and not tuned for anything else.
- Tables and colors adapt automatically to a light or dark chart background.
- Open-source. Every input has a plain-language label and tooltip, so reading Pine is not required to use it.
- For educational and informational purposes only. Not financial advice.
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Recession Warning Model [BackQuant]Recession Warning Model
Overview
The Recession Warning Model (RWM) is a Pine Script® indicator designed to estimate the probability of an economic recession by integrating multiple macroeconomic, market sentiment, and labor market indicators. It combines over a dozen data series into a transparent, adaptive, and actionable tool for traders, portfolio managers, and researchers. The model provides customizable complexity levels, display modes, and data processing options to accommodate various analytical requirements while ensuring robustness through dynamic weighting and regime-aware adjustments.
Purpose
The RWM fulfills the need for a concise yet comprehensive tool to monitor recession risk. Unlike approaches relying on a single metric, such as yield-curve inversion, or extensive economic reports, it consolidates multiple data sources into a single probability output. The model identifies active indicators, their confidence levels, and the current economic regime, enabling users to anticipate downturns and adjust strategies accordingly.
Core Features
- Indicator Families : Incorporates 13 indicators across five categories: Yield, Labor, Sentiment, Production, and Financial Stress.
- Dynamic Weighting : Adjusts indicator weights based on recent predictive accuracy, constrained within user-defined boundaries.
- Leading and Coincident Split : Separates early-warning (leading) and confirmatory (coincident) signals, with adjustable weighting (default 60/40 mix).
- Economic Regime Sensitivity : Modulates output sensitivity based on market conditions (Expansion, Late-Cycle, Stress, Crisis), using a composite of VIX, yield-curve, financial conditions, and credit spreads.
- Display Options : Supports four modes—Probability (0-100%), Binary (four risk bins), Lead/Coincident, and Ensemble (blended probability).
- Confidence Intervals : Reflects model stability, widening during high volatility or conflicting signals.
- Alerts : Configurable thresholds (Watch, Caution, Warning, Alert) with persistence filters to minimize false signals.
- Data Export : Enables CSV output for probabilities, signals, and regimes, facilitating external analysis in Python or R.
Model Complexity Levels
Users can select from four tiers to balance simplicity and depth:
1. Essential : Focuses on three core indicators—yield-curve spread, jobless claims, and unemployment change—for minimalistic monitoring.
2. Standard : Expands to nine indicators, adding consumer confidence, PMI, VIX, S&P 500 trend, money supply vs. GDP, and the Sahm Rule.
3. Professional : Includes all 13 indicators, incorporating financial conditions, credit spreads, JOLTS vacancies, and wage growth.
4. Research : Unlocks all indicators plus experimental settings for advanced users.
Key Indicators
Below is a summary of the 13 indicators, their data sources, and economic significance:
- Yield-Curve Spread : Difference between 10-year and 3-month Treasury yields. Negative spreads signal banking sector stress.
- Jobless Claims : Four-week moving average of unemployment claims. Sustained increases indicate rising layoffs.
- Unemployment Change : Three-month change in unemployment rate. Sharp rises often precede recessions.
- Sahm Rule : Triggers when unemployment rises 0.5% above its 12-month low, a reliable recession indicator.
- Consumer Confidence : University of Michigan survey. Declines reflect household pessimism, impacting spending.
- PMI : Purchasing Managers’ Index. Values below 50 indicate manufacturing contraction.
- VIX : CBOE Volatility Index. Elevated levels suggest market anticipation of economic distress.
- S&P 500 Growth : Weekly moving average trend. Declines reduce wealth effects, curbing consumption.
- M2 + GDP Trend : Monitors money supply and real GDP. Simultaneous declines signal credit contraction.
- NFCI : Chicago Fed’s National Financial Conditions Index. Positive values indicate tighter conditions.
- Credit Spreads : Proxy for corporate bond spreads using 10-year vs. 2-year Treasury yields. Widening spreads reflect stress.
- JOLTS Vacancies : Job openings data. Significant drops precede hiring slowdowns.
- Wage Growth : Year-over-year change in average hourly earnings. Late-cycle spikes often signal economic overheating.
Data Processing
- Rate of Change (ROC) : Optionally applied to capture momentum in data series (default: 21-bar period).
- Z-Score Normalization : Standardizes indicators to a common scale (default: 252-bar lookback).
- Smoothing : Applies a short moving average to final signals (default: 5-bar period) to reduce noise.
- Binary Signals : Generated for each indicator (e.g., yield-curve inverted or PMI below 50) based on thresholds or Z-score deviations.
Probability Calculation
1. Each indicator’s binary signal is weighted according to user settings or dynamic performance.
2. Weights are normalized to sum to 100% across active indicators.
3. Leading and coincident signals are aggregated separately (if split mode is enabled) and combined using the specified mix.
4. The probability is adjusted by a regime multiplier, amplifying risk during Stress or Crisis regimes.
5. Optional smoothing ensures stable outputs.
Display and Visualization
- Probability Mode : Plots a continuous 0-100% recession probability with color gradients and confidence bands.
- Binary Mode : Categorizes risk into four levels (Minimal, Watch, Caution, Alert) for simplified dashboards.
- Lead/Coincident Mode : Displays leading and coincident probabilities separately to track signal divergence.
- Ensemble Mode : Averages traditional and split probabilities for a balanced view.
- Regime Background : Color-coded overlays (green for Expansion, orange for Late-Cycle, amber for Stress, red for Crisis).
- Analytics Table : Optional dashboard showing probability, confidence, regime, and top indicator statuses.
Practical Applications
- Asset Allocation : Adjust equity or bond exposures based on sustained probability increases.
- Risk Management : Hedge portfolios with VIX futures or options during regime shifts to Stress or Crisis.
- Sector Rotation : Shift toward defensive sectors when coincident signals rise above 50%.
- Trading Filters : Disable short-term strategies during high-risk regimes.
- Event Timing : Scale positions ahead of high-impact data releases when probability and VIX are elevated.
Configuration Guidelines
- Enable ROC and Z-score for consistent indicator comparison unless raw data is preferred.
- Use dynamic weighting with at least one economic cycle of data for optimal performance.
- Monitor stress composite scores above 80 alongside probabilities above 70 for critical risk signals.
- Adjust adaptation speed (default: 0.1) to 0.2 during Crisis regimes for faster indicator prioritization.
- Combine RWM with complementary tools (e.g., liquidity metrics) for intraday or short-term trading.
Limitations
- Macro indicators lag intraday market moves, making RWM better suited for strategic rather than tactical trading.
- Historical data availability may constrain dynamic weighting on shorter timeframes.
- Model accuracy depends on the quality and timeliness of economic data feeds.
Final Note
The Recession Warning Model provides a disciplined framework for monitoring economic downturn risks. By integrating diverse indicators with transparent weighting and regime-aware adjustments, it empowers users to make informed decisions in portfolio management, risk hedging, or macroeconomic research. Regular review of model outputs alongside market-specific tools ensures its effective application across varying market conditions. Indicator
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Overnight Positioning w EMA - Strategy [presentTrading]I've recently started researching Market Timing strategies, and it’s proving to be quite an interesting area of study. The idea of predicting optimal times to enter and exit the market, based on historical data and various indicators, brings a dynamic edge to trading. Additionally, it is integrated with the 3commas bot for automated trade execution.
I'm still working on it. Welcome to share your point of view.
█ Introduction and How it is Different
The "Overnight Positioning with EMA " is designed to capitalize on market inefficiencies during the overnight trading period. This strategy takes a position shortly before the market closes and exits shortly after it opens the following day. What sets this strategy apart is the integration of an optional Exponential Moving Average (EMA) filter, which ensures that trades are aligned with the underlying trend. The strategy provides flexibility by allowing users to select between different global market sessions, such as the US, Asia, and Europe.
It is integrated with the 3commas bot for automated trade execution and has a built-in mechanism to avoid holding positions over the weekend by force-closing positions on Fridays before the market closes.
BTCUSD 20 mins Performance
█ Strategy, How it Works: Detailed Explanation
The core logic of this strategy is simple: enter trades before market close and exit them after market open, taking advantage of potential price movements during the overnight period. Here’s how it works in more detail:
🔶 Market Timing
The strategy determines the local market open and close times based on the selected market (US, Asia, Europe) and adjusts entry and exit points accordingly. The entry is triggered a specific number of minutes before market close, and the exit is triggered a specific number of minutes after market open.
🔶 EMA Filter
The strategy includes an optional EMA filter to help ensure that trades are taken in the direction of the prevailing trend. The EMA is calculated over a user-defined timeframe and length. The entry is only allowed if the closing price is above the EMA (for long positions), which helps to filter out trades that might go against the trend.
The EMA formula:
```
EMA(t) = +
```
Where:
- EMA(t) is the current EMA value
- Close(t) is the current closing price
- n is the length of the EMA
- EMA(t-1) is the previous period's EMA value
🔶 Entry Logic
The strategy monitors the market time in the selected timezone. Once the current time reaches the defined entry period (e.g., 20 minutes before market close), and the EMA condition is satisfied, a long position is entered.
- Entry time calculation:
```
entryTime = marketCloseTime - entryMinutesBeforeClose * 60 * 1000
```
🔶 Exit Logic
Exits are triggered based on a specified time after the market opens. The strategy checks if the current time is within the defined exit period (e.g., 20 minutes after market open) and closes any open long positions.
- Exit time calculation:
exitTime = marketOpenTime + exitMinutesAfterOpen * 60 * 1000
🔶 Force Close on Fridays
To avoid the risk of holding positions over the weekend, the strategy force-closes any open positions 5 minutes before the market close on Fridays.
- Force close logic:
isFriday = (dayofweek(currentTime, marketTimezone) == dayofweek.friday)
█ Trade Direction
This strategy is designed exclusively for long trades. It enters a long position before market close and exits the position after market open. There is no shorting involved in this strategy, and it focuses on capturing upward momentum during the overnight session.
█ Usage
This strategy is suitable for traders who want to take advantage of price movements that occur during the overnight period without holding positions for extended periods. It automates entry and exit times, ensuring that trades are placed at the appropriate times based on the market session selected by the user. The 3commas bot integration also allows for automated execution, making it ideal for traders who wish to set it and forget it. The strategy is flexible enough to work across various global markets, depending on the trader's preference.
█ Default Settings
1. entryMinutesBeforeClose (Default = 20 minutes):
This setting determines how many minutes before the market close the strategy will enter a long position. A shorter duration could mean missing out on potential movements, while a longer duration could expose the position to greater price fluctuations before the market closes.
2. exitMinutesAfterOpen (Default = 20 minutes):
This setting controls how many minutes after the market opens the position will be exited. A shorter exit time minimizes exposure to market volatility at the open, while a longer exit time could capture more of the overnight price movement.
3. emaLength (Default = 100):
The length of the EMA affects how the strategy filters trades. A shorter EMA (e.g., 50) reacts more quickly to price changes, allowing more frequent entries, while a longer EMA (e.g., 200) smooths out price action and only allows entries when there is a stronger underlying trend.
The effect of using a longer EMA (e.g., 200) would be:
```
EMA(t) = +
```
4. emaTimeframe (Default = 240):
This is the timeframe used for calculating the EMA. A higher timeframe (e.g., 360) would base entries on longer-term trends, while a shorter timeframe (e.g., 60) would respond more quickly to price movements, potentially allowing more frequent trades.
5. useEMA (Default = true):
This toggle enables or disables the EMA filter. When enabled, trades are only taken when the price is above the EMA. Disabling the EMA allows the strategy to enter trades without any trend validation, which could increase the number of trades but also increase risk.
6. Market Selection (Default = US):
This setting determines which global market's open and close times the strategy will use. The selection of the market affects the timing of entries and exits and should be chosen based on the user's preference or geographic focus. Strategy
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US Net LiquidityAnalysis of US Net Liquidity: A Comprehensive Overview
Introduction:
The "US Net Liquidity" indicator offers a detailed analysis of liquidity conditions within the United States, drawing insights from critical financial metrics related to the Federal Reserve (FED) and other government accounts. This tool enables economists to assess liquidity dynamics, identify trends, and inform economic decision-making.
Key Metrics and Interpretation:
1. Smoothing Period: This parameter adjusts the level of detail in the analysis by applying a moving average to the liquidity data. A longer smoothing period results in a smoother trend line, useful for identifying broader liquidity patterns over time.
2. Data Source (Timeframe): Specifies the timeframe of the data used for analysis, typically daily (D). Different timeframes can provide varying perspectives on liquidity trends.
3. Data Categories:
- FED Balance Sheet: Represents the assets and liabilities of the Federal Reserve, offering insights into monetary policy and market interventions.
- US Treasury General Account (TGA): Tracks the balance of the US Treasury's general account, reflecting government cash management and financial stability.
- Overnight Reverse Repurchase Agreements (RRP): Highlights short-term borrowing and lending operations between financial institutions and the Federal Reserve, influencing liquidity conditions.
- Earnings Remittances to the Treasury: Indicates revenues transferred to the US Treasury from various sources, impacting government cash flow and liquidity.
4. Moving Average Length: Determines the duration of the moving average applied to the data. A longer moving average length smoothens out short-term fluctuations, emphasizing longer-term liquidity trends.
Variation Lookback Length: Specifies the historical period used to assess changes and variations in liquidity. A longer lookback length captures more extended trends and fluctuations.
Interpretation:
1. Data Retrieval: Real-time data from specified financial instruments (assets) is retrieved to calculate balances for each category (FED, TGA, RRP, Earnings Remittances).
2. Global Balance Calculation: The global liquidity balance is computed by aggregating the balances of individual categories (FED Balance - TGA Balance - RRP Balance - Earnings Remittances Balance). This metric provides a comprehensive view of net liquidity.
3. Smoothed Global Balance (SMA): The Simple Moving Average (SMA) is applied to the global liquidity balance to enhance clarity and identify underlying trends. A rising SMA suggests improving liquidity conditions, while a declining SMA may indicate tightening liquidity.
Insight Generation and Decision-Making:
1. Trend Analysis: By analyzing smoothed liquidity trends over time, economists can identify periods of liquidity surplus or deficit, which can inform monetary policy decisions and market interventions.
2. Forecasting: Understanding liquidity dynamics aids in economic forecasting, particularly in predicting market liquidity, interest rate movements, and financial stability.
3. Policy Implications: Insights derived from this analysis tool can guide policymakers in formulating effective monetary policies, managing government cash flow, and ensuring financial stability.
Conclusion:
The "US Net Liquidity" analysis tool serves as a valuable resource for economists, offering a data-driven approach to understanding liquidity dynamics within the US economy. By interpreting key metrics and trends, economists can make informed decisions and contribute to macroeconomic stability and growth.
Disclaimer: This analysis is based on real-time financial data and should be used for informational purposes only. It is not intended as financial advice or a substitute for professional expertise. Indicator
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TSI Strength Meter vs USD with divergenceThis indicator consists of two lines. One is a gray line (USD) and the asset indicator is green or red.
The basis of this indicator is the true strength indicator (TSI) with parameters 5,15. Both line sets are based on a TSI (5,15).
The lookback period is for new highs / new lows. Default value is 200 periods.
GREEN/RED LINE
The first that is green and red is whatever you choose to display ( BTC in this case).
The green and red lines indicate going up or going down.
GRAY LINE
The gray line is the US Dollar . So everything is relative to that by default.
ZERO LINE CROSSES
These are momentum shifts. If you see a crossover of both around the zero line, its a good indication there is a change in momentum and a reversal of trend.
NEW HIGHS NEW LOWS
There are 4 new colors added to this indicator. For the asset you are viewing, a lime color means new highs within the lookback period. A new low is indicated by a yellow line color.
The new lows for the USD are white for new lows within the lookback period and blue line for the new highs.
DIVERGENCE
You can also spot divergences easily. For example, if a lime color is seen on the indicator line, that means "new high" but if it occurs below the last "new high" it means the asset is going up to new highs but the indicator is showing us that the readings are below the previous new highs, indicating a negative divergence.
The same goes for the yellow colored lines. higher yellows mean positive divergence.
And with the US Dollar , blue lines dropping means a negative divergence in the US Dollar , while white lines moving up means a positive dollar divergence.
INTERPRETATION
Examples:
If you see a green and sometimes red line of the asset indicator and a gray line that drops below the zero line; it may mean the asset is rising and the trend is up.
If you see a green and red line below the zero line and with a gray line above the zero line , it indicates there is a negative trend. If you suddenly see blue lines on the USD, this means its hitting new lows. If these blue lines then start to slowly move downwards; then we have a positive divergence. If that were to be followed by the green line crossing the zero line, its a pretty good be that the trend is changing and its a very good buying oportunity. Indicator
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