Overview
The Multi-Timeframe Trend Momentum Trading Strategy is a comprehensive trading system that integrates multi-timeframe trend analysis, momentum signals, volume confirmation, and smart money concepts (Change of Character [CHoCH] and Break of Structure [BOS]) to provide traders with a robust tool for capturing market trends while minimizing false signals. The strategy's unique "AI" component analyzes trends across multiple timeframes to provide a clear, actionable dashboard, making it accessible for both novice and experienced traders. The strategy is fully customizable, allowing users to tailor its filters to their trading style.
Strategy Principles
The strategy integrates multiple components to create a cohesive trading system:
Multi-Timeframe Trend Analysis: The strategy evaluates trends on three timeframes (1H, 4H, Daily) using Exponential Moving Averages (EMA) and Volume-Weighted Average Price (VWAP). A trend is considered bullish if the price is above both the EMA and VWAP, bearish if below, or neutral otherwise. Signals are only generated when the trend on the user-selected higher timeframe aligns with the trade direction (e.g., Buy signals require a bullish higher timeframe trend). This reduces noise and ensures trades follow the broader market context.
Momentum Filter: Measures the percentage price change between consecutive bars and compares it to a volatility-adjusted threshold (based on the Average True Range [ATR]). This ensures trades are taken only during significant price movements, filtering out low-momentum conditions.
Volume Filter (Optional): Checks if the current volume exceeds a long-term average and shows positive short-term volume change. This confirms strong market participation, reducing the risk of false breakouts.
Breakout Filter (Optional): Requires the price to break above (for Buy) or below (for Sell) recent highs/lows, ensuring the signal aligns with a structural shift in the market.
Smart Money Concepts (CHoCH/BOS):
- Change of Character (CHoCH): Detects potential reversals when the price crosses under a recent pivot high (for Sell) or over a recent pivot low (for Buy) with a bearish or bullish candle, respectively.
- Break of Structure (BOS): Confirms trend continuations when the price breaks below a recent pivot low (for Sell) or above a recent pivot high (for Buy) with strong momentum. These signals are plotted as horizontal lines with labels, making it easy to visualize key levels.
AI Trend Dashboard: Combines trend direction, momentum, and volatility (ATR) across timeframes to calculate a trend score. Scores above 0.5 indicate an "Up" trend, below -0.5 indicate a "Down" trend, and otherwise "Neutral." Displays a table summarizing trend strength (as a percentage), AI confidence (based on trend alignment), and Cumulative Volume Delta (CVD) for market context. A second table (optional) shows trend predictions for 1H, 4H, and Daily timeframes, helping traders anticipate future market direction.
Dynamic Trendlines: Plots support and resistance lines based on recent swing lows and highs within user-defined periods (shortTrendPeriod, longTrendPeriod). These lines adapt to market conditions and are colored based on trend strength.
Strategy Advantages
The Multi-Timeframe Trend Momentum Trading Strategy offers several significant advantages:
Reduces False Signals: By requiring confluence across trend, momentum, volume, and breakout filters, it minimizes trades in choppy or low-conviction markets.
Adapts to Market Context: The ATR-based momentum threshold adjusts dynamically to volatility, ensuring signals remain relevant in both trending and ranging markets.
Simplifies Decision-Making: The AI dashboard distills complex multi-timeframe data into a user-friendly table, eliminating the need for manual analysis.
Leverages Smart Money: CHoCH and BOS signals capture institutional price action patterns, giving traders an edge in identifying reversals and continuations.
Visual Clarity: The strategy makes market structure visible by marking key levels, signals, and trendlines, helping traders better understand price action.
Risk Management Built-In: Predefined take-profit and stop-loss levels facilitate disciplined risk management, which is crucial for long-term trading success.
Advanced Warning: With "Get Ready" signals, traders are alerted to potential setups before they fully develop, allowing more preparation and planning time.
Strategy Risks
Despite its many advantages, the strategy also presents some potential risks:
Parameter Optimization Trap: Over-optimizing strategy parameters may lead to curve-fitting that performs poorly in future market conditions. The solution is to conduct extensive backtesting across multiple markets and timeframes to find robust parameter sets.
Delayed Signals: The use of multiple filters may result in signals that lag price action, sometimes missing ideal entry points. The solution is to adjust parameters more sensitive to market speed, such as pivot length and momentum threshold.
Incorrect Trend Identification: In highly volatile or directionless markets, trend assessment may be inaccurate. The solution is to reduce trading during these conditions or increase the strictness of filter requirements.
Money Management Flaws: Fixed take-profit and stop-loss points may not be suitable for all market conditions. The solution is to adjust them to ATR-based values to adapt to current volatility.
Computation Intensity: The complexity of the strategy may cause performance issues on some platforms, especially when analyzing large amounts of historical data. The solution is to limit backtest time ranges or simplify non-critical calculations.
Data Dependency: The strategy relies on accurate multi-timeframe data, which may not be available in all trading environments. The solution is to implement reliable fallbacks, as shown in the code with local value calculations.
Preference for High Liquidity Markets: The strategy may produce more false signals on low liquidity markets. The solution is to focus on major currency pairs, widely held stocks, and major cryptocurrencies.
Strategy Optimization Directions
The strategy can be further optimized in several directions:
Adaptive Parameters: Implement automatic adjustment of parameters such as momentum threshold based on historical volatility data. This can improve the strategy's adaptability across different market conditions.
Machine Learning Integration: Apply machine learning algorithms to identify optimal parameter combinations or predict strategy performance under specific market conditions. This could be implemented by analyzing historical performance data, further enhancing the "AI" aspect of the strategy.
Market Sentiment Indicators: Add external market sentiment data, such as VIX index or social media sentiment analysis, to provide broader context for trading decisions. This can help the strategy avoid trading during extreme market conditions.
Time Filters: Add filters based on market volatility time patterns to avoid trading during known low-volatility periods (such as mid-Asian sessions). This can reduce the number of low-quality signals.
Correlation Analysis: Add cross-asset correlation checks to ensure trades align with movements in related markets (e.g., considering the Dollar Index when trading EUR/USD). This can provide additional signal confirmation.
Money Management Optimization: Implement volatility-based dynamic take-profit/stop-loss levels and add money management rules such as adjusting position sizes as the account grows. This will improve long-term risk-adjusted returns.
Performance Optimization: Streamline the code and reduce unnecessary calculations, particularly in trendline and table displays, to improve strategy responsiveness in real-time trading.
Data Agnosticism: Enhance the strategy to handle data interruptions or missing values more gracefully, ensuring robustness under less-than-ideal conditions.
Summary
The Multi-Timeframe Trend Momentum Trading Strategy provides a comprehensive trading system that combines traditional technical analysis, smart money concepts, and unique AI-driven trend analysis. Its strength lies in the multiple layers of filtering and confirmation that ensure trade signals are generated only in high-probability situations.
A particularly innovative aspect of the strategy is the integration of multi-timeframe trend information into an intuitive visual dashboard, enabling traders to quickly assess market conditions without complex manual analysis. The visualization of dynamic support/resistance lines and key structure levels further enhances this usability.
By intelligently combining CHoCH and BOS concepts, the strategy is able to capture subtle shifts in market psychology that often precede trend continuations or potential reversals. The use of ATR-adjusted thresholds ensures the strategy can adapt to different volatility conditions, making it suitable for various market environments.
Despite some risks and limitations, this already powerful system can be further enhanced through the suggested optimization measures. With wise application of risk management principles and parameter adjustments based on specific trading objectives and risk tolerance, the strategy has the potential to be a valuable tool in any trader's arsenal.
Ultimately, as with all trading strategies, success will depend on proper parameter optimization, disciplined execution, robust risk management, and a deep understanding of market dynamics.
Strategy source code
/*backtest
start: 2024-05-15 00:00:00
end: 2025-05-13 08:00:00
period: 1d
basePeriod: 1d
exchanges: [{"eid":"Futures_Binance","currency":"ETH_USDT"}]
*/
//@version=5
strategy("PowerHouse SwiftEdge AI v2.10 Strategy", overlay=true, calc_on_every_tick=true)
// Inputs with flexible settings
length = input.int(5, "Pivot Length", minval=1, maxval=20, step=1, tooltip="Number of bars to identify pivot highs and lows.")
momentum_threshold_base = input.float(0.01, "Base Momentum Threshold (%)", minval=0.001, maxval=1.0, step=0.001, tooltip="Base percentage change for signals.")
tp_points = input.int(10, "Take Profit (points)", minval=5, maxval=500, step=5)
sl_points = input.int(10, "Stop Loss (points)", minval=5, maxval=500, step=5)
min_signal_distance = input.int(5, "Min Signal Distance (bars)", minval=1, maxval=50, step=1)
tp_box_height = input.float(0.5, "TP Box Height % (Optional)", minval=0.1, maxval=2.0, step=0.1)
pre_momentum_factor_base = input.float(0.5, "Base Pre-Momentum Factor", minval=0.1, maxval=1.0, step=0.1, tooltip="Base factor for Get Ready signals.")
shortTrendPeriod = input.int(30, title="Short Trend Period", minval=10, maxval=100)
longTrendPeriod = input.int(100, title="Long Trend Period", minval=50, maxval=200)
// Custom filters
use_momentum_filter = input.bool(true, "Use Momentum Filter", group="Signal Filters", tooltip="Require price change to exceed momentum threshold.")
use_trend_filter = input.bool(true, "Use Higher Timeframe Trend Filter", group="Signal Filters", tooltip="Require alignment with the selected higher timeframe trend.")
higher_tf_choice = input.string("60", "Higher Timeframe", options=["60", "240", "D"], group="Signal Filters", tooltip="Choose the timeframe for the higher timeframe filter.")
use_lower_tf_filter = input.bool(true, "Use Lower Timeframe Filter", group="Signal Filters", tooltip="Prevent signals against the selected lower timeframe trend.")
lower_tf_choice = input.string("60", "Lower Timeframe", options=["60", "240", "D"], group="Signal Filters", tooltip="Choose the timeframe for the lower timeframe filter.")
use_volume_filter = input.bool(false, "Use Volume Filter", group="Signal Filters", tooltip="Require volume above average (optional).")
use_breakout_filter = input.bool(false, "Use Breakout Filter", group="Signal Filters", tooltip="Require price to break previous high/low (optional).")
show_get_ready = input.bool(true, "Show Get Ready Signals", group="Signal Filters", tooltip="Enable or disable Get Ready signals.")
restrict_repeated_signals = input.bool(false, "Restrict Repeated Signals", group="Signal Filters", tooltip="Prevent multiple signals in the same trend direction until trend changes.")
restrict_trend_tf_choice = input.string("60", "Restrict Trend Timeframe", options=["60", "240", "D"], group="Signal Filters", tooltip="Choose the timeframe to check trend for restricting repeated signals.")
enable_ai_analysis = input.bool(true, "Enable AI Market Analysis", group="AI Market Analysis", tooltip="Show AI predictions for future trends across timeframes.")
ai_table_position = input.string("Bottom Center", "AI Market Analysis Table Position", options=["Top Left", "Top Center", "Top Right", "Middle Left", "Middle Center", "Middle Right", "Bottom Left", "Bottom Center", "Bottom Right"], group="AI Market Analysis", tooltip="Choose the position of the AI Market Analysis table.")
// Additional inputs for optional filters
volumeLongPeriod = input.int(50, "Long Volume Period", minval=10, maxval=100, group="Volume Filter Settings")
volumeShortPeriod = input.int(5, "Short Volume Period", minval=1, maxval=20, group="Volume Filter Settings")
breakoutPeriod = input.int(5, "Breakout Period", minval=1, maxval=50, group="Breakout Filter Settings")
// Function to convert string to position
f_getTablePosition(position_string) =>
switch position_string
"Top Left" => position.top_left
"Top Center" => position.top_center
"Top Right" => position.top_right
"Middle Left" => position.middle_left
"Middle Center" => position.middle_center
"Middle Right" => position.middle_right
"Bottom Left" => position.bottom_left
"Bottom Center" => position.bottom_center
"Bottom Right" => position.bottom_right
=> position.middle_right // Standard fallback
// AI-powered adaptive signal adjustment with ATR
atr_raw = ta.atr(14)
atr = na(atr_raw) and bar_index > 0 ? (high - low) : atr_raw
volatility_factor = atr / close
momentum_threshold = momentum_threshold_base * (1 + volatility_factor * 2)
pre_momentum_factor = pre_momentum_factor_base * (1 - volatility_factor * 0.5)
pre_momentum_threshold = momentum_threshold * pre_momentum_factor
// Raw CVD calculation
var float raw_cvd = 0.0
delta_volume = close > close[1] ? volume : close < close[1] ? -volume : 0
raw_cvd := raw_cvd + delta_volume
// Volatility Context based on absolute CVD
cvd_level = math.abs(raw_cvd) < 10000 ? "Low" : math.abs(raw_cvd) < 50000 ? "Medium" : "High"
cvd_color = raw_cvd > 0 ? color.lime : raw_cvd < 0 ? color.red : color.yellow
// Calculate price change (momentum)
price_change = ((close - close[1]) / close[1]) * 100
// Find highs and lows
pivot_high = ta.pivothigh(high, length, length)
pivot_low = ta.pivotlow(low, length, length)
// Variables to store recent highs and lows
var float last_high = na
var float last_low = na
if not na(pivot_high)
last_high := pivot_high
if not na(pivot_low)
last_low := pivot_low
// Variables for signal control
var float choch_sell_level = na
var float choch_buy_level = na
var float bos_sell_level = na
var float bos_buy_level = na
var float tp_sell_level = na
var float tp_buy_level = na
var int last_signal_bar = -min_signal_distance - 1
var string last_signal = "Neutral"
var int last_trend = 0 // Tracks the last trend direction for restrict_repeated_signals
// Multi-timeframe trend analysis with robust fallback
// Calculate EMA and VWAP for each timeframe
ema60_raw = request.security(syminfo.tickerid, "60", ta.ema(close, 20), lookahead=barmerge.lookahead_on)
vwap60_raw = request.security(syminfo.tickerid, "60", ta.vwap(hlc3), lookahead=barmerge.lookahead_on)
ema240_raw = request.security(syminfo.tickerid, "240", ta.ema(close, 20), lookahead=barmerge.lookahead_on)
vwap240_raw = request.security(syminfo.tickerid, "240", ta.vwap(hlc3), lookahead=barmerge.lookahead_on)
emaD_raw = request.security(syminfo.tickerid, "D", ta.ema(close, 20), lookahead=barmerge.lookahead_on)
vwapD_raw = request.security(syminfo.tickerid, "D", ta.vwap(hlc3), lookahead=barmerge.lookahead_on)
// Local EMA and VWAP as fallback
local_ema = ta.ema(close, 20)
local_vwap = ta.vwap(hlc3)
// Fallback for missing data
ema60 = na(ema60_raw) ? local_ema : ema60_raw
vwap60 = na(vwap60_raw) ? local_vwap : vwap60_raw
ema240 = na(ema240_raw) ? local_ema : ema240_raw
vwap240 = na(vwap240_raw) ? local_vwap : vwap240_raw
emaD = na(emaD_raw) ? local_ema : emaD_raw
vwapD = na(vwapD_raw) ? local_vwap : vwapD_raw
// Trend determination (1 = up, -1 = down, 0 = neutral)
trend60 = close > ema60 and close > vwap60 ? 1 : close < ema60 and close < vwap60 ? -1 : 0
trend240 = close > ema240 and close > vwap240 ? 1 : close < ema240 and close < vwap240 ? -1 : 0
trendD = close > emaD and close > vwapD ? 1 : close < emaD and close < vwapD ? -1 : 0
// AI-Trend Strength (-100 til +100)
trend_strength_raw = trend60 + trend240 + trendD
trend_strength = (trend_strength_raw / 3) * 100
// AI Confidence (simulated success rate)
var float ai_confidence = 50.0
if trend_strength_raw == 3 or trend_strength_raw == -3
ai_confidence := 90.0
else if trend_strength_raw >= 2 or trend_strength_raw <= -2
ai_confidence := 75.0
else
ai_confidence := 60.0
// Filter conditions
// Dynamic selection of higher timeframe trend (for signal filtering)
var int higher_tf_trend = 0
switch higher_tf_choice
"60" => higher_tf_trend := trend60
"240" => higher_tf_trend := trend240
"D" => higher_tf_trend := trendD
bullish_trend_ok = higher_tf_trend == 1
bearish_trend_ok = higher_tf_trend == -1
// Dynamic selection of lower timeframe trend (for signal filtering)
var int lower_tf_trend = 0
switch lower_tf_choice
"60" => lower_tf_trend := trend60
"240" => lower_tf_trend := trend240
"D" => lower_tf_trend := trendD
lower_tf_bullish = lower_tf_trend == 1
lower_tf_bearish = lower_tf_trend == -1
lower_tf_not_neutral = lower_tf_trend != 0
// Dynamic selection of trend timeframe for restrict_repeated_signals
var int restrict_tf_trend = 0
switch restrict_trend_tf_choice
"60" => restrict_tf_trend := trend60
"240" => restrict_tf_trend := trend240
"D" => restrict_tf_trend := trendD
volAvg50 = ta.sma(volume, volumeLongPeriod)
volShort = ta.sma(volume, volumeShortPeriod)
volCondition = volume > volAvg50 and ta.change(volShort) > 0
highestBreakout = ta.highest(high, breakoutPeriod)
lowestBreakout = ta.lowest(low, breakoutPeriod)
// CHoCH and BOS definitions
choch_sell = ta.crossunder(low, last_high) and close < open
choch_buy = ta.crossover(high, last_low) and close > open
bos_sell = ta.crossunder(low, last_low[1]) and low < last_low[1] and close < open
bos_buy = ta.crossover(high, last_high[1]) and high > last_high[1] and close > open
// Signal conditions with selectable filters
early_sell_signal = use_momentum_filter ? price_change < -momentum_threshold : true
early_buy_signal = use_momentum_filter ? price_change > momentum_threshold : true
sell_trend_ok = use_trend_filter ? bearish_trend_ok : true
buy_trend_ok = use_trend_filter ? bullish_trend_ok : true
sell_lower_tf_ok = use_lower_tf_filter ? (not lower_tf_bullish and lower_tf_not_neutral) : true
buy_lower_tf_ok = use_lower_tf_filter ? (not lower_tf_bearish and lower_tf_not_neutral) : true
sell_volume_ok = use_volume_filter ? volCondition : true
buy_volume_ok = use_volume_filter ? volCondition : true
sell_breakout_ok = use_breakout_filter ? close < lowestBreakout[1] : true
buy_breakout_ok = use_breakout_filter ? close > highestBreakout[1] : true
// Logic to limit repeated signals based on restrict_tf_trend
sell_allowed = not restrict_repeated_signals or (last_signal != "Sell" or (last_signal == "Sell" and restrict_tf_trend != last_trend and restrict_tf_trend != -1))
buy_allowed = not restrict_repeated_signals or (last_signal != "Buy" or (last_signal == "Buy" and restrict_tf_trend != last_trend and restrict_tf_trend != 1))
sell_condition = early_sell_signal and (bar_index - last_signal_bar >= min_signal_distance) and sell_trend_ok and sell_lower_tf_ok and sell_volume_ok and sell_breakout_ok and sell_allowed
buy_condition = early_buy_signal and (bar_index - last_signal_bar >= min_signal_distance) and buy_trend_ok and buy_lower_tf_ok and buy_volume_ok and buy_breakout_ok and buy_allowed
get_ready_sell = use_momentum_filter ? (price_change < -pre_momentum_threshold and price_change > -momentum_threshold) : true and (bar_index - last_signal_bar >= min_signal_distance) and sell_trend_ok and sell_lower_tf_ok and sell_volume_ok and sell_breakout_ok
get_ready_buy = use_momentum_filter ? (price_change > pre_momentum_threshold and price_change < momentum_threshold) : true and (bar_index - last_signal_bar >= min_signal_distance) and buy_trend_ok and buy_lower_tf_ok and buy_volume_ok and buy_breakout_ok
// Strategy logic
if buy_condition
strategy.entry("Long", strategy.long)
strategy.exit("TP/SL Long", "Long", limit=close + tp_points, stop=close - sl_points)
label.new(bar_index, low, "Buy", color=color.green, style=label.style_label_up, textcolor=color.white)
tp_buy_level := high + tp_points
last_signal := "Buy"
last_signal_bar := bar_index
last_trend := restrict_tf_trend
if sell_condition
strategy.entry("Short", strategy.short)
strategy.exit("TP/SL Short", "Short", limit=close - tp_points, stop=close + sl_points)
label.new(bar_index, high, "Sell", color=color.red, style=label.style_label_down, textcolor=color.white)
tp_sell_level := low - tp_points
last_signal := "Sell"
last_signal_bar := bar_index
last_trend := restrict_tf_trend
// Plot Get Ready signals
if show_get_ready and get_ready_sell
label.new(bar_index, high, "Get Ready SELL", color=color.orange, style=label.style_label_down, textcolor=color.black, size=size.small)
if show_get_ready and get_ready_buy
label.new(bar_index, low, "Get Ready BUY", color=color.yellow, style=label.style_label_up, textcolor=color.black, size=size.small)
// Plot CHoCH and BOS as solid lines with AI-like colors
var line choch_sell_line = na
var line choch_buy_line = na
var line bos_sell_line = na
var line bos_buy_line = na
// Trendlines with buffer control and AI laser look
var line sup = na
var line res = na
if barstate.islast
float lowest_y2 = 60000
int lowest_x2 = 0
float highest_y2 = 0
int highest_x2 = 0
// Limit lookback to max 2000 bars to avoid buffer errors
int maxShortBars = math.min(math.min(shortTrendPeriod, bar_index), 2000)
for i = 1 to maxShortBars
if low[i] < lowest_y2
lowest_y2 := low[i]
lowest_x2 := i
if high[i] > highest_y2
highest_y2 := high[i]
highest_x2 := i
float lowest_y1 = 60000
int lowest_x1 = 0
float highest_y1 = 0
int highest_x1 = 0
// Limit lookback to max 2000 bars to avoid buffer errors
int maxLongBars = math.min(math.min(longTrendPeriod, bar_index), 2000)
for j = shortTrendPeriod + 1 to maxLongBars
if low[j] < lowest_y1
lowest_y1 := low[j]
lowest_x1 := j
if high[j] > highest_y1
highest_y1 := high[j]
highest_x1 := j
int trendStrength = trend_strength_raw
// Plot levels
plot(choch_sell_level, title="Last High at CHoCH", color=color.aqua, style=plot.style_circles, linewidth=1, trackprice=false)
plot(choch_buy_level, title="Last Low at CHoCH", color=color.lime, style=plot.style_circles, linewidth=1, trackprice=false)
plot(bos_sell_level, title="Last Low at BOS", color=color.fuchsia, style=plot.style_circles, linewidth=1, trackprice=false)
plot(bos_buy_level, title="Last High at BOS", color=color.teal, style=plot.style_circles, linewidth=1, trackprice=false)
plot(tp_sell_level, title="TP Sell", color=color.red, style=plot.style_circles, linewidth=1, trackprice=false)
plot(tp_buy_level, title="TP Buy", color=color.green, style=plot.style_circles, linewidth=1, trackprice=false)
plot(last_high, title="Last High", color=color.red, style=plot.style_histogram, linewidth=1, trackprice=true)
plot(last_low, title="Last Low", color=color.green, style=plot.style_histogram, linewidth=1, trackprice=true)
// AI Market Analysis - Calculate data regardless of condition
momentum_1h = request.security(syminfo.tickerid, "60", close - close[3], lookahead=barmerge.lookahead_on)
momentum_4h = request.security(syminfo.tickerid, "240", close - close[3], lookahead=barmerge.lookahead_on)
momentum_d = request.security(syminfo.tickerid, "D", close - close[3], lookahead=barmerge.lookahead_on)
// Calculate ATR and SMA(ATR) for each timeframe
atr_1h = request.security(syminfo.tickerid, "60", ta.atr(14), lookahead=barmerge.lookahead_on)
atr_4h = request.security(syminfo.tickerid, "240", ta.atr(14), lookahead=barmerge.lookahead_on)
atr_d = request.security(syminfo.tickerid, "D", ta.atr(14), lookahead=barmerge.lookahead_on)
sma_atr_1h = request.security(syminfo.tickerid, "60", ta.sma(ta.atr(14), 20), lookahead=barmerge.lookahead_on)
sma_atr_4h = request.security(syminfo.tickerid, "240", ta.sma(ta.atr(14), 20), lookahead=barmerge.lookahead_on)
sma_atr_d = request.security(syminfo.tickerid, "D", ta.sma(ta.atr(14), 20), lookahead=barmerge.lookahead_on)
// Local ATR and SMA(ATR) as fallback
local_atr = ta.atr(14)
local_sma_atr = ta.sma(ta.atr(14), 20)
// Fallback for missing data
volatility_1h = na(atr_1h) ? local_atr : atr_1h
volatility_4h = na(atr_4h) ? local_atr : atr_4h
volatility_d = na(atr_d) ? local_atr : atr_d
volatility_avg_1h = na(sma_atr_1h) ? local_sma_atr : sma_atr_1h
volatility_avg_4h = na(sma_atr_4h) ? local_sma_atr : sma_atr_4h
volatility_avg_d = na(sma_atr_d) ? local_sma_atr : sma_atr_d
momentum_1h := na(momentum_1h) ? 0 : momentum_1h
momentum_4h := na(momentum_4h) ? 0 : momentum_4h
momentum_d := na(momentum_d) ? 0 : momentum_d
// Analysis based on trend, momentum and volatility
score_1h = trend60 + (momentum_1h > 0 ? 0.5 : momentum_1h < 0 ? -0.5 : 0) + (volatility_1h > volatility_avg_1h ? 0.5 : 0)
score_4h = trend240 + (momentum_4h > 0 ? 0.5 : momentum_4h < 0 ? -0.5 : 0) + (volatility_4h > volatility_avg_4h ? 0.5 : 0)
score_d = trendD + (momentum_d > 0 ? 0.5 : momentum_d < 0 ? -0.5 : 0) + (volatility_d > volatility_avg_d ? 0.5 : 0)
// Predictions
pred_1h = score_1h > 0.5 ? "Up" : score_1h < -0.5 ? "Down" : "Neutral"
pred_4h = score_4h > 0.5 ? "Up" : score_4h < -0.5 ? "Down" : "Neutral"
pred_d = score_d > 0.5 ? "Up" : score_d < -0.5 ? "Down" : "Neutral"
// Futuristic AI Trend Dashboard
var table trendTable = table.new(position.top_right, columns=2, rows=6, bgcolor=color.new(color.black, 50), border_width=2, border_color=color.new(color.teal, 20))
table.cell(trendTable, 0, 0, "AI-Trend Matrix v2.10", text_color=color.new(color.aqua, 0), bgcolor=color.new(color.navy, 60))
table.cell(trendTable, 1, 0, "", bgcolor=color.new(color.navy, 60))
table.merge_cells(trendTable, 0, 0, 1, 0)
table.cell(trendTable, 0, 1, "Trend Strength", text_color=color.white)
table.cell(trendTable, 1, 1, str.tostring(math.round(trend_strength)), text_color=trend_strength > 0 ? color.rgb(0, math.min(255, trend_strength * 2.55), 0) : color.rgb(math.min(255, math.abs(trend_strength) * 2.55), 0, 0))
table.cell(trendTable, 0, 2, "AI Confidence", text_color=color.white)
table.cell(trendTable, 1, 2, str.tostring(ai_confidence) + "%", text_color=color.teal)
table.cell(trendTable, 0, 3, "AI Calibration", text_color=color.white)
table.cell(trendTable, 1, 3, "CVD: " + str.tostring(math.round(raw_cvd)) + " (" + cvd_level + ")", text_color=cvd_color)
table.cell(trendTable, 0, 4, "1H", text_color=color.white)
table.cell(trendTable, 1, 4, trend60 == 1 ? "Up" : trend60 == -1 ? "Down" : "Neutral", text_color=trend60 == 1 ? color.lime : trend60 == -1 ? color.fuchsia : color.yellow)
table.cell(trendTable, 0, 5, "4H", text_color=color.white)
table.cell(trendTable, 1, 5, trend240 == 1 ? "Up" : trend240 == -1 ? "Down" : "Neutral", text_color=trend240 == 1 ? color.lime : trend240 == -1 ? color.fuchsia : color.yellow)
// Table for AI Market Analysis
if enable_ai_analysis
var table ai_table = table.new(f_getTablePosition(ai_table_position), columns=4, rows=2, bgcolor=color.new(color.black, 50), border_width=2, border_color=color.new(color.teal, 20))
table.cell(ai_table, 0, 0, "AI Market Analysis", text_color=color.new(color.aqua, 0), bgcolor=color.new(color.navy, 60))
table.cell(ai_table, 1, 0, "1H", text_color=color.white)
table.cell(ai_table, 2, 0, "4H", text_color=color.white)
table.cell(ai_table, 3, 0, "1D", text_color=color.white)
table.cell(ai_table, 0, 1, "Prediction", text_color=color.white)
table.cell(ai_table, 1, 1, pred_1h, text_color=pred_1h == "Up" ? color.lime : pred_1h == "Down" ? color.fuchsia : color.yellow)
table.cell(ai_table, 2, 1, pred_4h, text_color=pred_4h == "Up" ? color.lime : pred_4h == "Down" ? color.fuchsia : color.yellow)
table.cell(ai_table, 3, 1, pred_d, text_color=pred_d == "Up" ? color.lime : pred_d == "Down" ? color.fuchsia : color.yellow)
// Debug alerts for null data
if na(ema60) or na(vwap60)
alert("Warning: 60-minute timeframe data is null!", alert.freq_once_per_bar)
if na(ema240) or na(vwap240)
alert("Warning: 240-minute timeframe data is null!", alert.freq_once_per_bar)
if na(emaD) or na(vwapD)
alert("Warning: Daily timeframe data is null!", alert.freq_once_per_bar)
Strategy parameters
The original address: Multi-Timeframe Trend Momentum Trading Strategy
Multi-timeframe trend trading? So you're basically stalking price action like an over-caffeinated detective! Love the momentum focus—until the 5-min chart yells 'BREAKOUT!' while the daily whispers 'fakeout...' Solid logic, but let's see those backtest results after a Fed meeting and a Twitter meltdown. No pressure!