Percentage Of Rising MA'sReturn the percentage of rising moving averages with periods in a custom range from min to max , with the possibility of using different types of moving averages.
Settings
Minimum MA Length Value : minimum period of the moving average.
Maximum MA Length Value : maximum period of the moving average.
Smooth : determine the period of an EMA using the indicator as input, 1 (no smoothing) by default.
Src : source input for the moving averages.
Type : type of the moving averages to be analyzed, available options are "SMA", "WMA" and "TMA", by default "SMA".
Usages
The indicator can return information about the main direction of a trend as well as its overall strength. A value of the indicator above 50 implies that more than 50% of the moving averages from period min to max are rising, this would suggest an uptrend, while a value inferior to 50 would suggest a down-trend.
On the chart, a ribbon consisting of simple moving averages from period 14 to 19, with a color indicating their direction, below the indicator with min = 14 and max = 19
The strength of a trend can be determined by how close the indicator is to 0 or 100, a value of 100 would imply that 100% percent of the moving averages are rising, this indicates a strong up-trend, while a value of 0 would suggest a strong down-trend.
Using different types of moving averages can allow to have more reactive or on the contrary, less noisy results.
Here the type of moving average used by both the ribbon and the indicator is the WMA, the WMA is more reactive than the SMA at the cost of providing less amount of filtering. On the other hand, using a triangular moving average (TMA) provide more filtering at the cost of being less reactive.
Finally, irregularities in the indicator output can be removed by using the smooth setting.
Above smooth = 50.
Details
The indicator is based upon a for loop, this implies that both the sma, wma or change functions are not directly usable, fortunately for us, it is possible to get the first difference of both the SMA, WMA and TMA without relying on a loop by using simple calculations.
The first difference of an SMA of period p is simply a momentum oscillator of period p divided by p , there are two ways to explain why this is the case, first, simple math can prove this, the first difference of an SMA is given by:
(x + x + ... + x )/p - (x + x + ... + x )/p
The repeating terms cancel each other out, as such, we end up with
(x - x )/p
which is simply a momentum oscillator divided by p , since this division doesn't change the sign of the output we can leave it out. We can also use impulses responses to prove this, the impulse response of a simple moving average is rectangular, taking the first difference of this impulse response will give the impulse response of a momentum oscillator, with the only difference being that the non-zero values of the result will be equal to 1/p instead of 1.
The same thing applies to the WMA
above the impulse response of the first difference of a WMA, we can see it is extremely similar to the one of a high pass SMA, only 1 bar longer, as such we can have the first difference of a WMA quite easily. The TMA is simply a 2 pass SMA (the SMA of an SMA), as such the solution is also simple.
在脚本中搜索"19亿美元是多少人民币"
Macroeconomic Artificial Neural Networks
This script was created by training 20 selected macroeconomic data to construct artificial neural networks on the S&P 500 index.
No technical analysis data were used.
The average error rate is 0.01.
In this respect, there is a strong relationship between the index and macroeconomic data.
Although it affects the whole world,I personally recommend using it under the following conditions: S&P 500 and related ETFs in 1W time-frame (TF = 1W SPX500USD, SP1!, SPY, SPX etc. )
Macroeconomic Parameters
Effective Federal Funds Rate (FEDFUNDS)
Initial Claims (ICSA)
Civilian Unemployment Rate (UNRATE)
10 Year Treasury Constant Maturity Rate (DGS10)
Gross Domestic Product , 1 Decimal (GDP)
Trade Weighted US Dollar Index : Major Currencies (DTWEXM)
Consumer Price Index For All Urban Consumers (CPIAUCSL)
M1 Money Stock (M1)
M2 Money Stock (M2)
2 - Year Treasury Constant Maturity Rate (DGS2)
30 Year Treasury Constant Maturity Rate (DGS30)
Industrial Production Index (INDPRO)
5-Year Treasury Constant Maturity Rate (FRED : DGS5)
Light Weight Vehicle Sales: Autos and Light Trucks (ALTSALES)
Civilian Employment Population Ratio (EMRATIO)
Capacity Utilization (TOTAL INDUSTRY) (TCU)
Average (Mean) Duration Of Unemployment (UEMPMEAN)
Manufacturing Employment Index (MAN_EMPL)
Manufacturers' New Orders (NEWORDER)
ISM Manufacturing Index (MAN : PMI)
Artificial Neural Network (ANN) Training Details :
Learning cycles: 16231
AutoSave cycles: 100
Grid
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 998
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Network
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Controls
Learning rate: 0.1000
Momentum: 0.8000 (Optimized)
Target error: 0.0100
Training error: 0.010000
NOTE : Alerts added . The red histogram represents the bear market and the green histogram represents the bull market.
Bars subject to region changes are shown as background colors. (Teal = Bull , Maroon = Bear Market )
I hope it will be useful in your studies and analysis, regards.
ANN MACD WTI (West Texas Intermediate) This script created by training WTI 4 hour data , 7 indicators and 12 Guppy Exponential Moving Averages.
Details :
Learning cycles: 1
AutoSave cycles: 100
Training error: 0.007593 ( Smaller than average target ! )
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 6
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
Special thanks to wroclai for his great effort.
Deep learning series will continue. But I need to rest my eyes a little :)
Stay tuned ! Regards.
ANN MACD BRENT CRUDE OIL (UKOIL) This script trained with Brent Crude Oil data including 7 basic indicators and 12 Guppy Exponential Moving Averages .
Details :
Learning cycles: 1
Training error: 0.006591 ( Smaller than 0.01 ! )
AutoSave cycles: 100
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 6
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
Note : Alerts added .
Special thanks to wroclai for his great effort.
Deep learning series will continue , stay tuned ! Regards.
ANN MACD GOLD (XAUUSD)This script aims to establish artificial neural networks with gold data.(4H)
Details :
Learning cycles: 329818
Training error: 0.012767 ( Slightly above average but negligible.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 5
Hidden layer 2 nodes: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
NOTE : Alarms added.
And special thanks to dear wroclai for his great effort.
Deep learning series will continue . Stay tuned! Regards.
ANN MACD S&P 500 This script is formed by training the S & P 500 Index with various indicators. Details :
Learning cycles: 78089
AutoSave cycles: 100
Training error: 0.011650 (Far less than the target, but acceptable.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Target error: 0.0100
Note : Thanks for dear wroclai for his great effort .
Deep learning series will continue . Stay tuned! Regards.
ANN MACD EURUSD (FX) Hello , this script is trained with eurusd 4-hour data. (550 columns) Details :
Learning cycles: 8327
AutoSave cycles: 100
Training error: 0.005500 ( That's a very good error coefficient.)
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 550
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 2
Hidden layer 2 nodes: 5
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.6000
Momentum: 0.8000
Target error: 0.0055
NOTE : Use with EURUSD only.
Alarms added.
Thanks dear wroclai for his great effort.
Deep learning series will continue ! Stay tuned.
Regards , Noldo .
ANN MACD ETHEREUM
This script is trained with Ethereum (Timeframe : 4 hours ).
Details :
Input columns: 19
Output columns: 1
Excluded columns: 0
Training example rows: 300
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 19
Hidden layer 1 nodes: 8
Hidden layer 2 nodes: 1
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate: 0.7000
Momentum: 0.8000
Training error: 0.009378 ( That's a very good error coefficient. )
Many thanks to wroclai for help.
Deep learning series will continue!
XPloRR MA-Trailing-Stop StrategyXPloRR MA-Trailing-Stop Strategy
Long term MA-Trailing-Stop strategy with Adjustable Signal Strength to beat Buy&Hold strategy
None of the strategies that I tested can beat the long term Buy&Hold strategy. That's the reason why I wrote this strategy.
Purpose: beat Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
My buy strategy is triggered by the fast buy EMA (blue) crossing over the slow buy SMA curve (orange) and the fast buy EMA has a certain up strength.
My sell strategy is triggered by either one of these conditions:
the EMA(6) of the close value is crossing under the trailing stop value (green) or
the fast sell EMA (navy) is crossing under the slow sell SMA curve (red) and the fast sell EMA has a certain down strength.
The trailing stop value (green) is set to a multiple of the ATR(15) value.
ATR(15) is the SMA(15) value of the difference between the high and low values.
The scripts shows a lot of graphical information:
The close value is shown in light-green. When the close value is lower then the buy value, the close value is shown in light-red. This way it is possible to evaluate the virtual losses during the trade.
the trailing stop value is shown in dark-green. When the sell value is lower then the buy value, the last color of the trade will be red (best viewed when zoomed)(in the example, there are 2 trades that end in gain and 2 in loss (red line at end))
the EMA and SMA values for both buy and sell signals are shown as a line
the buy and sell(close) signals are labeled in blue
How to use this strategy?
Every stock has it's own "DNA", so first thing to do is tune the right parameters to get the best strategy values voor EMA , SMA, Strength for both buy and sell and the Trailing Stop (#ATR).
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters)
Then keep using these parameters for future buy/sell signals only for that particular stock.
Do the same for other stocks.
Important : optimizing these parameters is no guarantee for future winning trades!
Here are the parameters:
Fast EMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 10-20)
Slow SMA Buy: buy trigger when Fast EMA Buy crosses over the Slow SMA Buy value (use values between 30-100)
Minimum Buy Strength: minimum upward trend value of the Fast SMA Buy value (directional coefficient)(use values between 0-120)
Fast EMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 10-20)
Slow SMA Sell: sell trigger when Fast EMA Sell crosses under the Slow SMA Sell value (use values between 30-100)
Minimum Sell Strength: minimum downward trend value of the Fast SMA Sell value (directional coefficient)(use values between 0-120)
Trailing Stop (#ATR): the trailing stop value as a multiple of the ATR(15) value (use values between 2-20)
Example parameters for different stocks (Start capital: 1000, Order=100% of equity, Period 1/1/2005 to now) compared to the Buy&Hold Strategy(=do nothing):
BEKB(Bekaert): EMA-Buy=12, SMA-Buy=44, Strength-Buy=65, EMA-Sell=12, SMA-Sell=55, Strength-Sell=120, Stop#ATR=20
NetProfit: 996%, #Trades: 6, %Profitable: 83%, Buy&HoldProfit: 78%
BAR(Barco): EMA-Buy=16, SMA-Buy=80, Strength-Buy=44, EMA-Sell=12, SMA-Sell=45, Strength-Sell=82, Stop#ATR=9
NetProfit: 385%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 55%
AAPL(Apple): EMA-Buy=12, SMA-Buy=45, Strength-Buy=40, EMA-Sell=19, SMA-Sell=45, Strength-Sell=106, Stop#ATR=8
NetProfit: 6900%, #Trades: 7, %Profitable: 71%, Buy&HoldProfit: 2938%
TNET(Telenet): EMA-Buy=12, SMA-Buy=45, Strength-Buy=27, EMA-Sell=19, SMA-Sell=45, Strength-Sell=70, Stop#ATR=14
NetProfit: 129%, #Trade
DTC Killzones ICT🕐 DTC Killzones ICT — Visualize Market Sessions Like a Pro
The DTC Killzones ICT indicator is a clean and intuitive tool designed for traders who want to analyze and visualize institutional trading sessions directly on their charts.
Inspired by ICT’s Killzone concept , this script makes it easy to identify overlapping market sessions — such as London, New York, and Asian — and track how price behaves within each zone.
💡 What It Does
This indicator automatically highlights key market sessions (Killzones) on your chart with fully customizable colors, labels, and transparency.
Each zone dynamically updates to reflect real-time highs and lows, helping you identify:
Session ranges and liquidity zones
Volatility windows and breakout areas
Institutional footprints across sessions
Whether you trade Forex, Indices, or Crypto , this script gives you visual clarity on when and where smart money is likely to move.
⚙️ Main Features
✅ Up to four customizable sessions (New York, London, Asian, and London Close)
✅ Adjustable timeframes and timezone options — sync with your exchange or custom UTC offset
✅ Dynamic high/low range tracking for each session
✅ Toggle range outlines, session labels , and transparency levels
✅ Optional daily dividers and session transition markers
✅ Works on any timeframe and any symbol
🧠 How Traders Use It
ICT-based traders can easily mark Killzones to align with setups like FVGs, liquidity grabs, or Silver Bullet entries.
Intraday traders can visualize session volatility and overlap periods for potential entries.
Swing traders can identify daily structure shifts by tracking range-to-range behavior.
🛠️ Customization
You can fully rename, recolor, or disable each session block.
Adjust the range transparency for visual comfort, and toggle session or daily dividers to fit your workflow.
Everything is designed to be clean, light, and modular — no clutter, no confusion.
⚡ Recommended Settings
For ICT-style analysis:
London Session: 02:00–05:00
New York Session: 07:00–10:00
Asian Session: 19:30–24:00
London Close Session: 10:00–12:00
These time windows are fully editable to suit your timezone or strategy.
🧩 Compatibility
Works seamlessly with TradingView’s built-in timezone tools
Compatible with all instruments and timeframes
Designed to overlay directly on your price chart
🏁 Final Notes
The DTC Killzones ICT indicator focuses purely on market session visualization — no alerts, entries, or trading signals.
It’s designed to complement your existing strategies and enhance clarity when analyzing market behavior across global sessions.
📈 Built for traders who value precision, structure, and timing.
cd_correlation_analys_Cxcd_correlation_analys_Cx
General:
This indicator is designed for correlation analysis by classifying stocks (487 in total) and indices (14 in total) traded on Borsa İstanbul (BIST) on a sectoral basis.
Tradingview's sector classifications (20) have been strictly adhered to for sector grouping.
Depending on user preference, the analysis can be performed within sectors, between sectors, or manually (single asset).
Let me express my gratitude to the code author, @fikira, beforehand; you will find the reason for my thanks in the context.
Details:
First, let's briefly mention how this indicator could have been prepared using the classic method before going into details.
Classically, assets could be divided into groups of forty (40), and the analysis could be performed using the built-in function:
ta.correlation(source1, source2, length) → series float.
I chose sectoral classification because I believe there would be a higher probability of assets moving together, rather than using fixed-number classes.
In this case, 21 arrays were formed with the following number of elements:
(3, 11, 21, 60, 29, 20, 12, 3, 31, 5, 10, 11, 6, 48, 73, 62, 16, 19, 13, 34 and indices (14)).
However, you might have noticed that some arrays have more than 40 elements. This is exactly where @Fikira's indicator came to the rescue. When I examined their excellent indicator, I saw that it could process 120 assets in a single operation. (I believe this was the first limit overrun; thanks again.)
It was amazing to see that data for 3 pairs could be called in a single request using a special method.
You can find the details here:
When I adapted it for BIST, I found it sufficient to call data for 2 pairs instead of 3 in a single go. Since asset prices are regular and have 2 decimal places, I used a fixed multiplier of $10^8$ and a fixed decimal count of 2 in Fikira's formulas.
With this method, the (high, low, open, close) values became accessible for each asset.
The summary up to this point is that instead of the ready-made formula + groups of 40, I used variable-sized groups and the method I will detail now.
Correlation/harmony/co-movement between assets provides advantages to market participants. Coherent assets are expected to rise or fall simultaneously.
Therefore, to convert co-movement into a mathematical value, I defined the possible movements of the current candle relative to the previous candle bar over a certain period (user-defined). These are:
Up := high > high and low > low
Down := high < high and low < low
Inside := high <= high and low >= low
Outside := high >= high and low <= low and NOT Inside.
Ignore := high = low = open = close
If both assets performed the same movement, 1 was added to the tracking counter.
If (Up-Up), (Down-Down), (Inside-Inside), or (Outside-Outside), then counter := counter + 1.
If the period length is 100 and the counter is 75, it means there is 75% co-movement.
Corr = counter / period ($75/100$)
Average = ta.sma(Corr, 100) is obtained.
The highest coefficients recorded in the array are presented to the user in a table.
From the user menu options, the user can choose to compare:
• With assets in its own sector
• With assets in the selected sector
• By activating the confirmation box and manually entering a single asset for comparison.
Table display options can be adjusted from the Settings tab.
In the attached examples:
Results for AKBNK stock from the Finance sector compared with GARAN stock from the same sector:
Timeframe: Daily, Period: 50 => Harmony 76% (They performed the same movement in 38 out of 50 bars)
Comment: Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Looking at ASELS from the Electronic Technology sector over the last 30 daily candles, they performed the same movements by 40% with XU100, 73.3% (22/30) with XUTEK (Technology Index), and 86.9% according to the averages.
Comment: It is more appropriate to follow ASELS stock with XUTEK (Technology index) instead of the general index (XU100). Opposite movements at swing high and low levels may indicate a change in the direction of the price flow (SMT).
Again, when ASELS stock is taken on H1 instead of daily, and the length is 100 instead of 30, the harmony rate is seen to be 87%.
Please share your thoughts and criticisms regarding the indicator, which I prepared with a bit of an educational purpose specifically for BIST.
Happy trading.
Supertrend with Coppock Curve and Dynamic Time WindowOverview
This indicator combines the **Supertrend** trend-following system with the **Coppock Curve** momentum oscillator to generate high-probability buy and sell signals. An additional **dynamic time window filter** ensures trades only occur during your specified trading hours, making it ideal for intraday traders who want to avoid low-liquidity periods.
How It Works
**Signal Generation:**
- **BUY Signal** (Green label below bar): Triggered when the Coppock Curve crosses above zero, the Supertrend confirms an uptrend, and the current time is within your specified trading window
- **SELL Signal** (Purple label above bar): Triggered when the Coppock Curve crosses below zero, the Supertrend confirms a downtrend, and the current time is within your specified trading window
**Triple Confirmation System:**
1. **Coppock Curve** - Identifies momentum shifts using rate-of-change calculations
2. **Supertrend** - Confirms the prevailing trend direction to filter false signals
3. **Time Window** - Ensures trades only occur during high-liquidity hours
Input Parameters
**Supertrend Settings:**
- **ATR Length** (Default: 19) - Period for calculating the Average True Range
- **Factor** (Default: 3.0) - Multiplier for ATR to determine Supertrend sensitivity
**Time Window Settings (Tehran Time UTC+3:30):**
- **Start Hour/Minute** (Default: 10:30) - Beginning of active trading window
- **End Hour/Minute** (Default: 22:30) - End of active trading window
Best Practices
- Works best on **trending markets** due to the Supertrend filter
- Recommended timeframes: **15min, 30min, 1H, 4H**
- Lower the Factor value (2.0-2.5) for more signals in volatile markets
- Increase the Factor value (3.5-4.0) for fewer, higher-quality signals in ranging markets
- Adjust the time window to match your market's peak liquidity hours
Risk Disclaimer
This indicator is for educational purposes only. Always use proper risk management, position sizing, and combine with your own analysis before making trading decisions.
XAUUSD 5m — CET 13:00→01:00 Supertrend + RSI (1:2 RR) — $240KThis strategy is designed for XAUUSD (Gold) on the 5-minute chart, optimized for trading during the most active hours (13:00–01:00 CET).
It combines a Supertrend direction filter with RSI crossovers for precise entries, and applies a 1:2 risk–reward ratio for consistent risk management.
🧠 Logic Overview:
Buy Signal: RSI crosses above 55 while Supertrend is bullish
Sell Signal: RSI crosses below 45 while Supertrend is bearish
Trading Hours: 13:00 → 01:00 CET (corresponding to 07:00 → 19:00 New York time)
Risk Management: Fixed 1:2 RR (TP = 2× SL distance from Supertrend line)
Session Management: Automatically closes all trades after 01:00 CET
Order Size: $240,000 notional exposure per position
💡 Best used for:
Scalping or intraday trading on XAUUSD during high-volatility hours.
The setup works best when combined with strong price action or volume confirmation.
⚠️ Disclaimer:
This script is for educational and testing purposes only.
Past performance does not guarantee future results.
Always test on demo before using live funds.
Highlight 15:00 to 19:00 candles CK - Indicator can used for back testing price movement on 1 hour timeframe for commodities
FX Sessions by m_cptForex Intraday Sessions Indicator, config time in UTC-4. Support 4 main sessions, smooth end-to-start candles mode, without gaps if your sessions has config like:
1) 19:00 - 03:00
2) 02:00 - 03:00
3) 03:00 -11:00
No excluded last candles issue on all TFs.
Working on LTF up to 1h TF since its intraday sessions indicator.
LANZ Origins🔷 LANZ Origins – Multi-Framework Liquidity, Structure & Risk Management Overlay
LANZ Origins is an advanced multi-framework visualization toolkit that unifies key institutional concepts into one efficient interface. Designed for professional traders, it merges session mapping, liquidity analysis, imbalance detection, multi-account risk control, and higher-timeframe candle tracing — all in a single overlay.
🧩 Core Components
🈵 Asian Range Liquidity
Automatically detects and projects the Asian session range (19:00–02:00 NY) with an optional mid-price line (50 %). This provides visual context for intraday liquidity and manipulation zones commonly referenced in ICT-style analysis.
📊 Imbalance Detector
Highlights Fair Value Gaps (FVG), Opening Gaps (OG), and Volume Imbalances (VI) directly on-chart, using separate color schemes for bullish and bearish inefficiencies. Each element can be customized by width, ATR filter, and extension length.
🕯️ Higher-Timeframe Candles (ICT Style)
Displays multi-timeframe candles (HTF1–HTF6) simultaneously — e.g., 5 m, 30 m, 1 h, 4 h, 1 D, 1 W — each rendered with independent wick, border, and fill settings. Includes remaining-time counters, timeframe labels, and optional imbalance shading between bodies.
📈 Market Structure (ZigZag 30 m)
Replicates 30-minute swing structure to all active timeframes, producing dynamic pivots with live extension. Ideal for contextualizing BOS/CHoCH events across multiple scales.
💸 Multi-Account Lot Size Panel
Calculates position size for up to five accounts simultaneously, using your defined capital, risk %, and fixed SL distance (in pips). Results appear in a clean table at the bottom-right corner of the chart.
🎨 Session Visualization
Colored backgrounds mark key trading phases:
🟢 Day division
🔴 No-action zone
🔵 Kill-zone
🟡 Hold session
⚙️ Customization & Performance
Every module can be toggled individually, with full color, opacity, and style control. The script is optimized for overlay use and supports up to 500 boxes, lines, and labels with efficient resource handling.
🧠 Best Use Case
LANZ Origins is ideal for traders who follow:
Smart Money Concepts / ICT methodology
Liquidity & Imbalance-based trading
Multi-timeframe confluence setups
Risk-based position sizing workflows
Use it to observe how price interacts with liquidity pools, higher-timeframe candles, and imbalances within key sessions — while monitoring lot size risk in real time.
📌 Recommended Setup
Timeframes: 30m - 5m – 3m
Pairs: FX
Session Timezone: New York (EST/EDT)
Combine with: LANZ Strategy series for execution and journaling
💬 Note
This indicator does not generate buy/sell signals. It’s a visual and analytical tool built to support your own decision-making process.
PAMASHAIn this version of 19 OCT 001 UPDATE, this Indicator forecast the future by indicating Hidden divergences and regular Divergences. Besides, it will distinguish order blocks, FVGs, ... .
ICT Killzones & MacrosICT Killzones & Macros (v1.1.5) — configurable ICT session windows + refined “macro” windows with live High/Low levels, optional extensions, next-window previews, and lightweight opening-price lines. Built to be clock-robust, timezone-aware, and performant on intraday charts.
Tip: All times are interpreted in your chosen IANA timezone (default: America/New_York) and auto-handle DST. You can rename, recolor, enable/disable, and retime every window.
What it plots
- Killzones (5) : Asia (19:00–02:00), London (02:00–05:00), NY AM (07:00–09:30), London Close (10:00–12:00), NY PM (13:30–16:00) — full-height boxes with optional header.
- Macros (8) (defaults tailored for common ICT “refined” windows): Asia-1 (18:00–21:00), Asia-2 (21:00–00:00), London-1 (01:00–04:00), AM-1 (09:45–10:15), AM-2 (10:45–11:15), Lunch (12:00–13:00), PM-1 (13:30–14:30), Power Hour (15:10–16:00).
- Live High/Low lines for the current Macro/Killzone window.
- Optional HL extension to the right until price crosses or the trading day rolls (style selectable).
- “Next” previews : earliest upcoming Macro and Killzone header; optional next-window background band.
- Opening Prices (3 lightweight time lines) : defaults 00:00, 08:30, 09:30 with right-edge labels, scoped to a session you choose (auto-cleans at session end).
- Key inputs & styling
- General : Timezone (IANA), “Sessions to show” (per window) to keep only the last N completed windows.
- Header : height (ticks), gap (ticks), fill opacity, border width/style, text size/color, toggle “Next Macro/Killzone” headers.
- Boxes : global fill opacity, global border width/style (used by both Macros & Killzones).
- High/Low : show HL, HL line style, extend on/off + extension style, optional extension labels.
- Opening Prices : enable Time 1/2/3, set HH:MM for each, session window, per-line colors, style (dotted/dashed/solid), width.
- Per-window controls : each Macro/Killzone has Enable, Session (HHMM-HHMM), Label, Fill color.
How to use (quick start)
- Set Timezone to your preference (default America/New_York).
- Toggle on the Macros and Killzones you trade. Adjust session times if needed.
- (Optional) Turn on Extend High/Low to project levels until crossed/day-roll.
- (Optional) Enable Next… headers to see the next upcoming window at a glance.
- (Optional) Configure Opening Prices (00:00 / 08:30 / 09:30 by default) and the session over which they appear.
Behavior & notes
- Time windows are computed by clock, not by guessing bar timestamps, making them robust across brokers and timeframes.
- With HL extension on, the current window’s levels extend until crossed or the end of the trading day (in your timezone). With it off, completed windows keep static HL markers (limited by “Sessions to show”).
- “Sessions to show” applies per Macro/Killzone to automatically prune older windows and keep charts snappy.
- Opening-price lines exist only within the chosen “Opening Prices Session” and are removed when it ends (keeps charts clean).
Defaults (color cues)
Killzones: Asia (blue), London (purple), NY AM (green), London Close (yellow), NY PM (orange).
Macros: neutral greys with Lunch and PM accents out of the box (all customizable).
Performance tips
- Reduce “Sessions to show” if you scroll far back in history.
- Disable “Next…” previews and/or extension labels on very slow machines.
- Narrow the “Opening Prices Session” window to exactly when you need those lines.
Changelog highlights
- v1.1.5 : Internal refinements and stability.
- v1.1.3 : Live High/Low lines for current windows + optional extension.
- v1.1.2 : Added “next Killzone” preview (to match “next Macro”).
- v1.1.0 : Defaults updated (5 KZ, 8 Macros). Removed “snap-to-killzone” behavior.
- v1.0.0 : Independent Macro vs. Killzone rendering; cleaner header logic.
- Known limitations
If your chart warns about drawings, trim “Sessions to show”.
If your broker session times differ from NY hours, adjust the sessions or change the indicator timezone.
Credits & intent
Inspired by ICT timing concepts; provided for education/mark-up, not financial advice.
Built to be flexible so you can mirror your personal playbook and journaling workflow.
Bitcoin Cycle History Visualization [SwissAlgo]BTC 4-Year Cycle Tops & Bottoms
Historical visualization of Bitcoin's market cycles from 2010 to present, with projections based on weighted averages of past performance.
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CALCULATION METHODOLOGY
Why Bottom-to-Bottom Cycle Measurement?
This indicator defines cycles as bottom-to-bottom periods. This is one of several valid approaches to Bitcoin cycle analysis:
- Focuses on market behavior (price bottoms) rather than supply schedule events (halving-to-halving)
- Bottoms may offer good reference points for some analytical purposes
- Tops tend to be extended periods that are harder to define precisely
- Aligns with how some traditional asset cycles are measured and the timing observed in the broader "risk-on" assets category
- Halving events are shown separately (yellow backgrounds) for reference
- Neither halving-based nor bottom-based measurement is inherently superior
Different analysts prefer different cycle definitions based on their analytical goals. This approach prioritizes observable market turning points.
Cycle Date Definitions
- Approximate monthly ranges used for each event (e.g., Nov 2022 bottom = Nov 1-30, 2022)
- Cycle 1: Jul 2010 bottom → Jun 2011 top → Nov 2011 bottom
- Cycle 2: Nov 2011 bottom → Dec 2013 top → Jan 2015 bottom
- Cycle 3: Jan 2015 bottom → Dec 2017 top → Dec 2018 bottom
- Cycle 4: Dec 2018 bottom → Nov 2021 top → Nov 2022 bottom
- Future cycles will be added as new top/bottom dates become firm
Duration Calculations
- Days = timestamp difference converted to days (milliseconds ÷ 86,400,000)
- Bottom → Top: days from cycle bottom to peak
- Top → Bottom: days from peak to next cycle bottom
- Bottom → Bottom: full cycle duration (sum of above)
Price Change Calculations
- % Change = ((New Price - Old Price) / Old Price) × 100
- Example: $200 → $19,700 = ((19,700 - 200) / 200) × 100 = 9,750% gain
- Approximate historical prices used (rounded to significant figures)
Weighted Average Formula
Recent cycles weighted more heavily to reflect the evolved market structure:
- Cycle 1 (2010-2011): EXCLUDED (too early-stage, tiny market cap)
- Cycle 2 (2011-2015): Weight = 1x
- Cycle 3 (2015-2018): Weight = 3x
- Cycle 4 (2018-2022): Weight = 5x
Formula: Weighted Avg = (C2×1 + C3×3 + C4×5) / (1+3+5)
Example for Bottom→Top days: (761×1 + 1065×3 + 1066×5) / 9 = 1,032 days
Projection Method
- Projected Top Date = Nov 2022 bottom + weighted avg Bottom→Top days
- Projected Bottom Date = Nov 2022 bottom + weighted avg Bottom→Bottom days
- Current days elapsed compared to weighted averages
- Warning symbol (⚠) shown when the current cycle exceeds the historical average
Technical Implementation
- Historical cycle dates are hardcoded (not algorithmically detected)
- Dates represent approximate monthly ranges for each event
- The indicator will be updated as the Cycle 5 top and bottom dates become confirmed
- Updates require manual code maintenance - not automatic
- Users should verify they're using the latest version for current cycle data
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FEATURES
- Background highlights for historical tops (red), bottoms (green), and halving events (yellow)
- Data table showing cycle durations and price changes
- Visual cycle boundary boxes with subtle coloring
- Projected timeframes displayed as dashed vertical lines
- Toggle on/off for each visual element
- Customizable background colors
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DISPLAY SETTINGS
- Show/hide cycle tops, bottoms, halvings, data table, and cycle boxes
- Customizable background colors for each event type
- Clean, institutional-grade visual design suitable for analysis
UPDATES & MAINTENANCE
This indicator is maintained as new cycle events occur. When Cycle 5's top and bottom are confirmed with sufficient time elapsed, the code and projections will be updated accordingly. Check for the latest version periodically.
OPEN SOURCE
Code available for review, modification, and improvement. Educational transparency is prioritized.
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IMPORTANT LIMITATIONS
⚠ EXTREMELY SMALL SAMPLE SIZE
Based on only 4 complete cycles (2011-2022). In statistical analysis, this is insufficient for reliable predictions.
⚠ CHANGED MARKET STRUCTURE
Bitcoin's market has fundamentally evolved since early cycles:
- 2010-2015: Tiny market cap, retail-only, unregulated
- 2024-2025: Institutional adoption, spot ETFs, regulatory frameworks, macro correlation
The environment that created past patterns no longer exists in the same form.
⚠ NO PREDICTIVE GUARANTEE
Historical patterns can and do break. Market cycles are not laws of physics. Past performance does not guarantee future results. The next cycle may not follow historical averages.
⚠ LENGTHENING CYCLE THEORY
Some analysts believe cycles are extending over time (diminishing returns, maturing market). If true, simple averaging underestimates future cycle lengths.
⚠ SELF-FULFILLING PROPHECY RISK
The halving narrative may be partially circular - it works because people believe it works. Sufficient changes in market structure or participant behavior can invalidate the pattern.
⚠ APPROXIMATE DATA
Historical prices rounded to significant figures. Exact bottom/top dates vary by exchange. Month-long ranges are used for simplicity.
EDUCATIONAL USE ONLY
This indicator is designed for historical analysis and understanding Bitcoin's past behavior. It is NOT:
- Trading advice or financial recommendations
- A guarantee or prediction of future price movements
- Suitable as a sole basis for investment decisions
- A replacement for fundamental or technical analysis
The projections show "what if the pattern continues exactly" - not "what will happen."
Always conduct independent research, understand the risks, and consult qualified financial advisors before making investment decisions. Only invest what you can afford to lose.
DTM 444 BANDS 🚀DTM 444 BANDS 🚀:
The DTM 444 BANDS 🚀 is a powerful, multi-purpose trading indicator combining Supertrend, Dynamic Band Levels, Breakout Signals, and Volume Confirmation to help traders identify high-probability trade setups across different timeframes.
🔧 Key Features
✅ Multi-Timeframe Support
Analyze price action across any timeframe using the Timeframe input.
All band calculations (High, Low, Midline, and Supertrend) are pulled from a higher timeframe for clearer context.
✅ Dynamic Bands Based on Supertrend
High Band: Rolling highest of Supertrend over hiLen period.
Low Band: Rolling lowest of Supertrend over loLen period.
Midline: Midpoint of the above.
Acts like dynamic support/resistance, ideal for trend-following and breakout strategies.
✅ Dual Signal System
Breakout Signals (Buy and Sell): Triggered when price breaks the bands with volume confirmation.
Supertrend Crossover Signals (Buy1 and Sell1): Classic momentum entries with a confirmation twist.
Exit Signals: Optional take-profit/neutral indicators when price reverses.
✅ Volume Confirmation Filter (Optional)
Only triggers signals if the volume exceeds its 20-period SMA.
Helps filter out false breakouts and weak trends in low-liquidity periods.
✅ Visual Enhancements
Color-coded candles based on band positioning (e.g., red = weak, green = strong, etc.)
On-chart labels for each signal for quick reference.
Real-time Signal Dashboard using Pine Script tables showing:
Current signal
Volume filter status
Live volume vs volume SMA
🧪 Practical Use Cases
Trend Traders: Use the Supertrend cross and band breakouts to ride trends early.
Breakout Traders: Catch high-probability moves outside established ranges.
Swing Traders: Time entries and exits using color-coded bars and exit labels.
Volume-Sensitive Traders: Focus on trades with strong volume backing.
📊 Backtest Snapshot
Based on the example chart for Reliance Industries (RELIANCE.NS) on the weekly timeframe:
Several profitable buy and breakout signals during uptrends.
Timely exits and breakdown alerts before reversals.
Volume filter keeps trades clean and avoids noise.
⚙️ Customizable Parameters
High Length and Low Length (default: 19)
Supertrend Multiplier and ATR Length
Volume Filter: Toggle ON/OFF
Volume SMA Length: Default 20
Custom Timeframe: Choose any higher timeframe for multi-timeframe analysis
📢 Alerts Ready
Fully integrated with TradingView alerts:
Breakout & Breakdown
Supertrend crossovers
All alerts respect the volume filter setting
🏁 Final Thoughts
DTM 444 BANDS 🚀 is a versatile and adaptive trading system that blends trend analysis, volatility bands, and volume validation. Whether you're a trend trader, breakout hunter, or swing trader — this tool gives you a structured edge with clear visual cues and real-time alerts.
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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AVGO Advanced Day Trading Strategy📈 Overview
The AVGO Advanced Day Trading Strategy is a comprehensive, multi-timeframe trading system designed for active day traders seeking consistent performance with robust risk management. Originally optimized for AVGO (Broadcom), this strategy adapts well to other liquid stocks and can be customized for various trading styles.
🎯 Key Features
Multiple Entry Methods
EMA Crossover: Classic trend-following signals using fast (9) and medium (16) EMAs
MACD + RSI Confluence: Momentum-based entries combining MACD crossovers with RSI positioning
Price Momentum: Consecutive price action patterns with EMA and RSI confirmation
Hybrid System: Advanced multi-trigger approach combining all methodologies
Advanced Technical Arsenal
When enabled, the strategy analyzes 8+ additional indicators for confluence:
Volume Price Trend (VPT): Measures volume-weighted price momentum
On-Balance Volume (OBV): Tracks cumulative volume flow
Accumulation/Distribution Line: Identifies institutional money flow
Williams %R: Momentum oscillator for entry timing
Rate of Change Suite: Multi-timeframe momentum analysis (5, 14, 18 periods)
Commodity Channel Index (CCI): Cyclical turning points
Average Directional Index (ADX): Trend strength measurement
Parabolic SAR: Dynamic support/resistance levels
🛡️ Risk Management System
Position Sizing
Risk-based position sizing (default 1% per trade)
Maximum position limits (default 25% of equity)
Daily loss limits with automatic position closure
Multiple Profit Targets
Target 1: 1.5% gain (50% position exit)
Target 2: 2.5% gain (30% position exit)
Target 3: 3.6% gain (20% position exit)
Configurable exit percentages and target levels
Stop Loss Protection
ATR-based or percentage-based stop losses
Optional trailing stops
Dynamic stop adjustment based on market volatility
📊 Technical Specifications
Primary Indicators
EMAs: 9 (Fast), 16 (Medium), 50 (Long)
VWAP: Volume-weighted average price filter
RSI: 6-period momentum oscillator
MACD: 8/13/5 configuration for faster signals
Volume Confirmation
Volume filter requiring 1.6x average volume
19-period volume moving average baseline
Optional volume confirmation bypass
Market Structure Analysis
Bollinger Bands (20-period, 2.0 multiplier)
Squeeze detection for breakout opportunities
Fractal and pivot point analysis
⏰ Trading Hours & Filters
Time Management
Configurable trading hours (default: 9:30 AM - 3:30 PM EST)
Weekend and holiday filtering
Session-based trade management
Market Condition Filters
Trend alignment requirements
VWAP positioning filters
Volatility-based entry conditions
📱 Visual Features
Information Dashboard
Real-time display of:
Current entry method and signals
Bullish/bearish signal counts
RSI and MACD status
Trend direction and strength
Position status and P&L
Volume and time filter status
Chart Visualization
EMA plots with customizable colors
Entry signal markers
Target and stop level lines
Background color coding for trends
Optional Bollinger Bands and SAR display
🔔 Alert System
Entry Alerts
Customizable alerts for long and short entries
Method-specific alert messages
Signal confluence notifications
Advanced Alerts
Strong confluence threshold alerts
Custom alert messages with signal counts
Risk management alerts
⚙️ Customization Options
Strategy Parameters
Enable/disable long or short trades
Adjustable risk parameters
Multiple entry method selection
Advanced indicator on/off toggle
Visual Customization
Color schemes for all indicators
Dashboard position and size options
Show/hide various chart elements
Background color preferences
📋 Default Settings
Initial Capital: $100,000
Commission: 0.1%
Default Position Size: 10% of equity
Risk Per Trade: 1.0%
RSI Length: 6 periods
MACD: 8/13/5 configuration
Stop Loss: 1.1% or ATR-based
🎯 Best Use Cases
Day Trading: Designed for intraday opportunities
Swing Trading: Adaptable for longer-term positions
Momentum Trading: Excellent for trending markets
Risk-Conscious Trading: Built-in risk management protocols
⚠️ Important Notes
Paper Trading Recommended: Test thoroughly before live trading
Market Conditions: Performance varies with market volatility
Customization: Adjust parameters based on your risk tolerance
Educational Purpose: Use as a learning tool and customize for your needs
🏆 Performance Features
Detailed performance metrics
Trade-by-trade analysis capability
Customizable risk/reward ratios
Comprehensive backtesting support
This strategy is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and consider your financial situation before trading.
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
