GAPfor myself
I say to you today, my friends, so even though we face the difficulties of today and tomorrow, I still have a dream. It is a dream deeply rooted in the American dream.
Penunjuk dan strategi
Multi-Timeframe EMA Bundle (576/676/144/169/12)A comprehensive EMA (Exponential Moving Average) indicator combining five key moving averages used by professional traders for trend identification and dynamic support/resistance levels.
Included EMAs:
EMA 576 & EMA 676 (Blue) — Long-term trend filters commonly used on lower timeframes to represent higher timeframe structure. Acts as major support/resistance zones.
EMA 144 & EMA 169 (White) — Mid-term trend indicators derived from Fibonacci numbers. When price respects this zone, it often signals strong trend continuation.
EMA 12 (Yellow) — Short-term momentum tracker for entries and exits. Useful for identifying pullback opportunities within the trend.
CSS Reversal - VAThis indicator identifies a price action reversal pattern known as CSS (Candle Stop Setup). Unlike standard 3-candle patterns, this logic is dynamic and "hunts" for the true peak or valley before confirming a shift in momentum.
Core Logic & Rules
The script follows a specific sequence of "Initiation, Waiting, and Triggering" to ensure it captures high-probability reversals:
1. Initiation (The Sweep): The process starts when a candle (the Pivot) sweeps the liquidity of the previous candle.
Bearish: Candle 2 makes a higher high than Candle 1.
Bullish: Candle 2 makes a lower low than Candle 1.
2. Identifying the Extreme: The script tracks the absolute highest high (for bearish) or lowest low (for bullish) during the setup. If a subsequent candle goes higher/lower without triggering a close, the "mark" moves to that new extreme candle.
3. The Waiting Room (Inside Bars): The setup remains active even if several candles follow that do not break out of the Pivot's range. The script can wait indefinitely (e.g., 3, 4, or 5+ candles) as long as the original extreme is not breached.
4. The Trigger (The Confirmation): A signal is only confirmed when a candle closes past the opposite side of the extreme candle's body.
Bearish Trigger: A candle closes below the Low of the highest candle.
Bullish Trigger: A candle closes above the High of the lowest candle.
5. Retrospective Marking: Once the trigger close occurs, the script automatically places a visual marker (arrow) on the actual extreme candle (the peak or valley), even if that candle occurred several bars ago.
Visual Indicators
Red Arrow (↓): Placed at the high of the highest candle in a confirmed bearish reversal.
Green Arrow (↑): Placed at the low of the lowest candle in a confirmed bullish reversal.
Use Cases
This script is designed for traders who look for Liquidity Sweeps and Market Structure Shifts. It filters out "fake" reversals where price merely wicks past a level without a solid closing confirmation, and it specifically accounts for "inside bar" periods where price consolidates before making its move.
PA Helper - SessionsPA Helper – Sessions is a session visualization and timing utility designed to give precise intraday context directly on your chart. It allows you to define up to four custom trading sessions with full control over time ranges, colors, fills, and timezone handling, making it suitable for any market or trading style.
The indicator draws clear session start and end markers and can optionally highlight the full session range on the chart. A key capability is that it does not limit itself to historical data only: it can project and display future session boundaries ahead of the current price, allowing you to see upcoming session starts and ends before they happen. This is especially useful for planning trades, managing exposure, and preparing for session-based volatility.
Candles outside active session hours can be visually muted or hidden using a separate color, helping you focus only on relevant trading periods. The script also supports real-time session detection, ensuring accuracy both in replay mode and live markets.
In addition, PA Helper – Sessions includes configurable alerts that can notify you a chosen number of minutes before a session starts or before it ends. These notifications work across all enabled sessions and are designed to trigger reliably without duplicate alerts, making them practical for active intraday traders.
Overall, this tool acts as a clean, precise session framework that combines past, present, and future session awareness, helping you align price action analysis with time-based market behavior.
Sonic R 89 - NY SL Custom Fixed//@version=5
indicator("Sonic R 89 - NY SL Custom Fixed", overlay=true, max_lines_count=500)
// --- 0. TÙY CHỈNH THÔNG SỐ ---
group_session = "Cài đặt Phiên Giao Dịch (Giờ New York)"
use_session = input.bool(true, "Chỉ giao dịch theo khung giờ", group=group_session)
session_time = input.session("0800-1200", "Khung giờ NY 1", group=group_session)
session_time2 = input.session("1300-1700", "Khung giờ NY 2", group=group_session)
max_trades_per_session = input.int(1, "Số lệnh tối đa/mỗi khung giờ", minval=1, group=group_session)
group_risk = "Quản lý Rủi ro (Dashboard)"
risk_usd = input.float(100.0, "Số tiền rủi ro mỗi lệnh ($)", minval=1.0, group=group_risk)
group_sl_custom = "Cấu hình Stop Loss (SL)"
sl_mode = input.string("Dragon", "Chế độ SL", options= , group=group_sl_custom)
lookback_x = input.int(5, "Số nến (X) cho Swing SL", minval=1, group=group_sl_custom)
group_htf = "Lọc Đa khung thời gian (MTF)"
htf_res = input.timeframe("30", "Chọn khung HTF", group=group_htf)
group_sonic = "Cấu hình Sonic R"
vol_mult = input.float(1.5, "Đột biến Volume", minval=1.0)
max_waves = input.int(4, "Ưu tiên n nhịp đầu", minval=1)
trade_cd = input.int(5, "Khoảng cách lệnh (nến)", minval=1)
group_tp = "Quản lý SL/TP & Dòng kẻ"
rr_tp1 = input.float(1.0, "TP1 (RR)", step=0.1)
rr_tp2 = input.float(2.0, "TP2 (RR)", step=0.1)
rr_tp3 = input.float(3.0, "TP3 (RR)", step=0.1)
rr_tp4 = input.float(4.0, "TP4 (RR)", step=0.1)
line_len = input.int(15, "Chiều dài dòng kẻ", minval=1)
// --- 1. KIỂM TRA PHIÊN & HTF ---
is_in_sess1 = not na(time(timeframe.period, session_time, "America/New_York"))
is_in_sess2 = not na(time(timeframe.period, session_time2, "America/New_York"))
is_in_session = use_session ? (is_in_sess1 or is_in_sess2) : true
var int trades_count = 0
is_new_session = is_in_session and not is_in_session
if is_new_session
trades_count := 0
htf_open = request.security(syminfo.tickerid, htf_res, open, lookahead=barmerge.lookahead_on)
htf_close = request.security(syminfo.tickerid, htf_res, close, lookahead=barmerge.lookahead_on)
is_htf_trend = htf_close >= htf_open ? 1 : -1
// --- 2. TÍNH TOÁN CHỈ BÁO ---
ema89 = ta.ema(close, 89)
ema34H = ta.ema(high, 34)
ema34L = ta.ema(low, 34)
atr = ta.atr(14)
avgVol = ta.sma(volume, 20)
slope89 = (ema89 - ema89 ) / atr
hasSlope = math.abs(slope89) > 0.12
isSqueezed = math.abs(ta.ema(close, 34) - ema89) < (atr * 0.5)
var int waveCount = 0
if not hasSlope
waveCount := 0
newWave = hasSlope and ((low <= ema34H and close > ema34H) or (high >= ema34L and close < ema34L))
if newWave and not newWave
waveCount := waveCount + 1
// --- 3. LOGIC VÀO LỆNH ---
isMarubozu = math.abs(close - open) / (high - low) > 0.8
highVol = volume > avgVol * vol_mult
buyCondition = is_in_session and (trades_count < max_trades_per_session) and waveCount <= max_waves and is_htf_trend == 1 and
(isMarubozu or highVol) and close > ema34H and low >= ema89 and
(slope89 > 0.1 or isSqueezed ) and close > open
sellCondition = is_in_session and (trades_count < max_trades_per_session) and waveCount <= max_waves and is_htf_trend == -1 and
(isMarubozu or highVol) and close < ema34L and high <= ema89 and
(slope89 < -0.1 or isSqueezed ) and close < open
// --- 4. QUẢN LÝ LỆNH ---
var float last_entry = na
var float last_sl = na
var float last_tp1 = na
var float last_tp2 = na
var float last_tp3 = na
var float last_tp4 = na
var string last_type = "NONE"
var int lastBar = 0
trigger_buy = buyCondition and (bar_index - lastBar > trade_cd)
trigger_sell = sellCondition and (bar_index - lastBar > trade_cd)
// --- 5. TÍNH TOÁN SL & LOT SIZE ---
float contract_size = 1.0
if str.contains(syminfo.ticker, "XAU") or str.contains(syminfo.ticker, "GOLD")
contract_size := 100
// Logic tính SL linh hoạt
float swing_low = ta.lowest(low, lookback_x)
float swing_high = ta.highest(high, lookback_x)
float temp_sl_calc = na
if trigger_buy
temp_sl_calc := (sl_mode == "Dragon") ? ema34L : swing_low
if trigger_sell
temp_sl_calc := (sl_mode == "Dragon") ? ema34H : swing_high
float sl_dist_calc = math.abs(close - temp_sl_calc)
float calc_lots = (sl_dist_calc > 0) ? (risk_usd / (sl_dist_calc * contract_size)) : 0
if (trigger_buy or trigger_sell)
trades_count := trades_count + 1
lastBar := bar_index
last_type := trigger_buy ? "BUY" : "SELL"
last_entry := close
last_sl := temp_sl_calc
float riskAmt = math.abs(last_entry - last_sl)
last_tp1 := trigger_buy ? last_entry + (riskAmt * rr_tp1) : last_entry - (riskAmt * rr_tp1)
last_tp2 := trigger_buy ? last_entry + (riskAmt * rr_tp2) : last_entry - (riskAmt * rr_tp2)
last_tp3 := trigger_buy ? last_entry + (riskAmt * rr_tp3) : last_entry - (riskAmt * rr_tp3)
last_tp4 := trigger_buy ? last_entry + (riskAmt * rr_tp4) : last_entry - (riskAmt * rr_tp4)
// Vẽ dòng kẻ
line.new(bar_index, last_entry, bar_index + line_len, last_entry, color=color.new(color.gray, 50), width=2)
line.new(bar_index, last_sl, bar_index + line_len, last_sl, color=color.red, width=2, style=line.style_dashed)
line.new(bar_index, last_tp1, bar_index + line_len, last_tp1, color=color.green, width=1)
line.new(bar_index, last_tp2, bar_index + line_len, last_tp2, color=color.lime, width=1)
line.new(bar_index, last_tp3, bar_index + line_len, last_tp3, color=color.aqua, width=1)
line.new(bar_index, last_tp4, bar_index + line_len, last_tp4, color=color.blue, width=2)
// KÍCH HOẠT ALERT()
string alert_msg = (trigger_buy ? "BUY " : "SELL ") + syminfo.ticker + " at " + str.tostring(close) + " | SL Mode: " + sl_mode + " | Lot: " + str.tostring(calc_lots, "#.##") + " | SL: " + str.tostring(last_sl, format.mintick)
alert(alert_msg, alert.freq_once_per_bar_close)
// --- 6. CẢNH BÁO CỐ ĐỊNH ---
alertcondition(trigger_buy, title="Sonic R BUY Alert", message="Sonic R BUY Signal Detected")
alertcondition(trigger_sell, title="Sonic R SELL Alert", message="Sonic R SELL Signal Detected")
// --- 7. DASHBOARD & PLOT ---
var table sonic_table = table.new(position.top_right, 2, 10, bgcolor=color.new(color.black, 70), border_width=1, border_color=color.gray)
if barstate.islast
table.cell(sonic_table, 0, 0, "NY SESSION", text_color=color.white), table.cell(sonic_table, 1, 0, last_type, text_color=(last_type == "BUY" ? color.lime : color.red))
table.cell(sonic_table, 0, 1, "SL Mode:", text_color=color.white), table.cell(sonic_table, 1, 1, sl_mode, text_color=color.orange)
table.cell(sonic_table, 0, 2, "Trades this Sess:", text_color=color.white), table.cell(sonic_table, 1, 2, str.tostring(trades_count) + "/" + str.tostring(max_trades_per_session), text_color=color.yellow)
table.cell(sonic_table, 0, 3, "LOT SIZE:", text_color=color.orange), table.cell(sonic_table, 1, 3, str.tostring(calc_lots, "#.##"), text_color=color.orange)
table.cell(sonic_table, 0, 4, "Entry:", text_color=color.white), table.cell(sonic_table, 1, 4, str.tostring(last_entry, format.mintick), text_color=color.yellow)
table.cell(sonic_table, 0, 5, "SL:", text_color=color.white), table.cell(sonic_table, 1, 5, str.tostring(last_sl, format.mintick), text_color=color.red)
table.cell(sonic_table, 0, 6, "TP1:", text_color=color.gray), table.cell(sonic_table, 1, 6, str.tostring(last_tp1, format.mintick), text_color=color.green)
table.cell(sonic_table, 0, 7, "TP2:", text_color=color.gray), table.cell(sonic_table, 1, 7, str.tostring(last_tp2, format.mintick), text_color=color.lime)
table.cell(sonic_table, 0, 8, "TP3:", text_color=color.gray), table.cell(sonic_table, 1, 8, str.tostring(last_tp3, format.mintick), text_color=color.aqua)
table.cell(sonic_table, 0, 9, "TP4:", text_color=color.gray), table.cell(sonic_table, 1, 9, str.tostring(last_tp4, format.mintick), text_color=color.blue)
plot(ema89, color=slope89 > 0.1 ? color.lime : slope89 < -0.1 ? color.red : color.gray, linewidth=2)
p_high = plot(ema34H, color=color.new(color.blue, 80))
p_low = plot(ema34L, color=color.new(color.blue, 80))
fill(p_high, p_low, color=color.new(color.blue, 96))
plotshape(trigger_buy, "BUY", shape.triangleup, location.belowbar, color=color.green, size=size.small)
plotshape(trigger_sell, "SELL", shape.triangledown, location.abovebar, color=color.red, size=size.small)
bgcolor(isSqueezed ? color.new(color.yellow, 92) : na)
bgcolor(not is_in_session ? color.new(color.gray, 96) : na)
AI Adaptive Trend Navigator Strategy Echo EditionAI Adaptive Trend Navigator Strategy
This is a professional long-only automated strategy optimized for Taiwan Index Futures (TX). Based on the LuxAlgo clustering framework, this version features advanced logic iteration for institutional-grade backtesting and execution.
1. Realistic Cost Modeling To ensure backtest reliability, this strategy is pre-configured with:
Slippage: 2 ticks (Approx. 400 TWD per side).
Commission: 100 TWD per side.
Total Cost: 500 TWD per side. This provides a rigorous stress test for real-world trading environments.
2. State Consistency & Logic Continuity Optimized the underlying array handling to ensure "State Persistence." This eliminates the logic gaps common in real-time script execution, ensuring that historical signals are 100% consistent with live alerts.
3. Adaptive AI Clustering Utilizes K-means clustering to dynamically select the optimal ATR factors based on current market volatility, allowing the strategy to "evolve" as market regimes shift.
🧠 開發理念：追求實戰一致性的量化策略 本策略旨在為台指期（TX）提供一套具備真實參考價值的自動化系統。
✨ Echo 版核心優化點
數據連續性迭代：修正底層邏輯，確保訊號在即時盤勢中穩定不跳斷。
真實交易成本模擬：預設 2 點滑價 與 單邊 100 TWD 手續費，單邊總成本對標 500 TWD，拒絕虛假神單，挑戰最嚴苛的回測環境。
台指期專屬參數調校：融入針對台灣市場波動特性的預設參數與過濾邏輯。
🛡️ 進階實戰過濾
空間緩衝區 (Buffer Strategy)：價格需有效突破緩衝區才觸發，精準過濾盤整雜訊。
AI 信心評分系統：只有當動能穩定度達標時才會發進場訊號。
冷卻保護機制：有效抑制訊號在洗盤區間過度頻繁跳動。
⚠️ Disclaimer: Backtest results do not guarantee future performance.
Euro Day StrategyThis is a false breakout reversal strategy that fades short-term breakouts when they conflict with longer-term momentum. Here's the detailed breakdown:
Strategy Overview
Type: Counter-trend/Fade strategy disguised as breakout trading
Core Logic: Enter against immediate breakouts when longer-term momentum suggests the move is exhausted.
Strategy Classification
This is a FADE/EXHAUSTION strategy, NOT a breakout-following strategy
Enters against the immediate breakout direction
Bets on mean reversion when short-term price action diverges from longer-term momentum
Works best in ranging/choppy markets where breakouts frequently fail
Will get hurt in strong trending markets where breakouts are genuine
This strategy is designed for intraday mean-reversion trading on instruments that tend to range (likely forex or futures). It requires markets where false breakouts are common and price tends to snap back quickly.
GLOBAL 3H SCALPING (BTC FILTER)글로벌 멀티 세션 & BTC 필터 고강도 스캘핑 알고리즘 기술 보고서
파인 스크립트 v5의 기술적 패러다임과 알고리즘 트레이딩의 진화
금융 시장의 디지털화가 가속화됨에 따라 개인 트레이더와 기관 투자자 모두 정교한 알고리즘을 활용하여 시장의 비효율성을 포착하려는 시도를 지속하고 있다. 파인 스크립트 v5는 네임스페이스 기반 아키텍처를 도입하여 코드의 가독성과 실행 효율성을 극대화하였습니다. 본 보고서에서는 기존 코드의 구문 오류를 수정하고, 아시아·유럽·미국 세션 및 비트코인(BTC) 커플링 필터를 포함한 최적화된 스크립트를 제공합니다.
🚀 GLOBAL 3H SCALPING (BTC FILTER) 전체 코드
이 코드는 모든 세션(아시아/유럽/미국)의 3시간 골든 아워를 포착하며, 비트코인의 추세가 알트코인과 일치할 때만 신호를 생성하는 '커플링 필터'가 내장된 최종 버전입니다.
Pine Script
//@version=5
indicator("GLOBAL 3H SCALPING (BTC FILTERED)", overlay=true, max_lines_count=300, max_labels_count=100)
//────────────────────
// ⏰ 세션 정의 (한국 시간 KST 기준)
//────────────────────
string tz = "Asia/Seoul"
string asiaSess = "0900-1200"
string euSess = "1600-1900"
string usSess = "2300-0200"
f_getFocus(sessionStr) =>
inSess = not na(time(timeframe.period, sessionStr, tz))
start = inSess and not nz(inSess , false)
float tfInSec = timeframe.in_seconds()
int bars3H = math.max(1, math.round(10800 / tfInSec))
int barsSinceStart = ta.barssince(start)
bool focus = inSess and (not na(barsSinceStart) and barsSinceStart < bars3H)
focus
bool asiaFocus = f_getFocus(asiaSess)
bool euFocus = f_getFocus(euSess)
bool usFocus = f_getFocus(usSess)
bool totalFocus = asiaFocus or euFocus or usFocus
bgcolor(asiaFocus? color.new(color.green, 92) : na, title="Asia Focus")
bgcolor(euFocus? color.new(color.blue, 92) : na, title="EU Focus")
bgcolor(usFocus? color.new(color.red, 92) : na, title="US Focus")
//────────────────────
// 🟠 BTC 커플링 필터 (BTC Trend Filter)
//────────────────────
// 비트코인의 추세를 실시간으로 가져와 알트코인 매매의 안전장치로 활용함
float btcPrice = request.security("BINANCE:BTCUSDT", timeframe.period, close)
float btcEMA = request.security("BINANCE:BTCUSDT", timeframe.period, ta.ema(close, 200))
bool btcBullish = btcPrice > btcEMA
bool btcBearish = btcPrice < btcEMA
//────────────────────
// 📈 기술적 지표 (Altcoin 자체 지표)
//────────────────────
float ema200 = ta.ema(close, 200)
plot(ema200, title="EMA200", color=color.new(color.yellow, 0), linewidth=2)
float vwapVal = ta.vwap(hlc3)
plot(vwapVal, title="VWAP", color=color.new(color.aqua, 0), linewidth=2)
float volMA = ta.sma(volume, 20)
bool volOK = volume > volMA
bool longVWAP = low <= vwapVal and close > vwapVal
bool shortVWAP = high >= vwapVal and close < vwapVal
//────────────────────
// 🚀 진입 조건 (BTC 필터 통합)
//────────────────────
bool longCond = totalFocus and close > ema200 and close > vwapVal and longVWAP and volOK and btcBullish
bool shortCond = totalFocus and close < ema200 and close < vwapVal and shortVWAP and volOK and btcBearish
plotshape(longCond, title="LONG", location=location.belowbar, style=shape.triangleup, size=size.small, color=color.lime, text="LONG")
plotshape(shortCond, title="SHORT", location=location.abovebar, style=shape.triangledown, size=size.small, color=color.red, text="SHORT")
//────────────────────
// 🧠 실시간 통합 대시보드
//────────────────────
var label infoLabel = na
if barstate.islast
label.delete(infoLabel)
string sessName = asiaFocus? "ASIA" : euFocus? "EUROPE" : usFocus? "US" : "WAITING"
string labelText = "GLOBAL ALGO (BTC FILTERED) 🌍 " +
"-------------------------- " +
"Active Session: " + sessName + " " +
"BTC Trend: " + (btcBullish? "BULLISH 🟢" : "BEARISH 🔴") + " " +
"Alt Trend: " + (close > ema200? "BULLISH" : "BEARISH") + " " +
"Volume: " + (volOK? "STRONG" : "WEAK")
infoLabel := label.new(
x = bar_index,
y = high,
text = labelText,
style = label.style_label_left,
color = color.new(color.black, 20),
textcolor = color.white
)
📘 Comprehensive User Manual (EN/KR)
1. English: Multi-Session & BTC Filtered Scalping Guide
Core Philosophy
The "Golden Hours" strategy focuses on the first 3 hours of global market openings when volatility and liquidity are at their peak . By filtering altcoin signals with the Bitcoin (BTC) trend, we ensure high-probability entries aligned with the overall market momentum .
Session Schedule (Korea Standard Time - KST)
The indicator highlights three major trading windows :
Asia Focus (Green): 09:00 – 12:00 KST (Tokyo/Seoul opening).
Europe Focus (Blue): 16:00 – 19:00 KST (London opening).
US Focus (Red): 23:00 – 02:00 KST (New York opening).
Trading Rules
Long (Buy) Entry Conditions:
Zone: Price must be within one of the colored Focus Zones.
BTC Filter: BTC must be trading above its EMA 200 (Market Sentiment: Bullish) .
Alt Trend: Altcoin price must be above its own EMA 200.
Value: Price is above VWAP.
Reaction: Candle low touches or dips below VWAP, then closes above it (Pullback) .
Volume: Current volume is higher than the 20-period average.
Short (Sell) Entry Conditions:
Zone: Price must be within one of the colored Focus Zones.
BTC Filter: BTC must be trading below its EMA 200 (Market Sentiment: Bearish).
Alt Trend: Altcoin price must be below its EMA 200.
Value: Price is below VWAP.
Reaction: Candle high touches or goes above VWAP, then closes below it (Rejection).
Volume: Current volume is higher than the 20-period average.
Professional Risk Management
1% Rule: Never risk more than 1% of your total capital on a single trade .
Leverage: Use 1x–5x for beginners, and 5x–20x for advanced traders only with tight stop-losses .
Stop-Loss: Place stop-losses 0.1%–0.5% away from the entry point or at the most recent swing high/low .
MTF Equals v1.0MTF Equals is a professional-grade tool designed to identify significant price levels across multiple timeframes. It scans the current chart and higher timeframes (HTF) for identical highs and lows ("Equals"), which often act as price magnets or liquidity pools.
Key Features:
Multi-Timeframe Analysis: Automatically detects identical highs and lows on the current chart, as well as M5, M15, M30, H1, H4, and Daily.
NQ Auto-Detection: Specialized logic for Nasdaq (NQ) that automatically determines the ideal starting point for analysis based on volume, efficiency, and price density.
Live Statistics: Displays the number of touches and the bar distance from the first touchpoint directly on the price level.
Smart Cleaning: Levels are automatically removed once they are significantly breached by price, keeping your chart clutter-free.
Advanced Visuals: Fully customizable colors, line styles, and label positioning (e.g., Align to Margin).
How to use:
Perfect for spotting "Equal Highs/Lows" (Liquidity) or confirming institutional support and resistance zones.
devendra Verma 3 SMA3 SMA RSI based can work to know the volatility and movement in the trend
can try to see the crosses of each other to generate buy and sell signals
Pendiente EMA20This indicator measures the slope of the 20-period Exponential Moving Average (EMA20) to objectively determine whether it is rising, falling, or flat, removing the need for visual guesswork.
The slope is calculated as the difference between the current EMA20 value and its value N bars ago.
This provides a simple and reliable way to filter market conditions:
Positive slope → EMA20 is rising (bullish bias)
Negative slope → EMA20 is falling (bearish bias)
Near zero → EMA20 is flat (range / no clear trend)
The background color highlights these states for quick visual confirmation.
Notes
A zero threshold is included by default and may need adjustment depending on the asset and timeframe.
For better robustness in ranging markets, consider normalizing the slope using ATR or ticks.
This indicator is intended as a trend filter, not as a standalone trading system.
SGX/GIFT Nifty Non-Indian Hours BoxThis scripts draws a box around the high and low of both the PRE-MARKET and POST-MARKET hours of SGX Nifty.
AI Adaptive Trend Navigator Echo EditionAI Adaptive Trend Navigator
This is an advanced trend-following system optimized for high-volatility index futures (TX). Built upon the LuxAlgo clustering framework, this version introduces several critical enhancements to meet professional trading standards:
1. State Consistency Iteration Enhanced the underlying logic for dynamic arrays and User-Defined Types (UDTs) to ensure stable "State Persistence." This fix eliminates logic gaps during real-time price fluctuations, ensuring that historical backtests perfectly align with live execution.
2. Adaptive Factor Tuning (K-means) The system simulates dozens of parameter paths in real-time, using K-means clustering to automatically select the optimal factor suited for the current market volatility.
3. Advanced Practical Filters
Dynamic Buffer Strategy: Filters out market noise during consolidation and early session volatility.
Confidence Threshold: Only triggers signals when the AI performance score meets the required quality.
Cooldown Logic: Prevents rapid signal flipping in choppy markets.
🧠 開發理念：將 AI 自適應力帶入台指期實戰 針對台指期（TX）高波動特性開發，透過機器學習演算法動態尋優，解決傳統指標參數固定的滯後性。
✨ Echo 版核心優化點
數據連續性迭代：底層邏輯優化，確保訊號在即時盤勢中穩定不跳斷，回測與實戰高度吻合。
自適應動態尋優：透過 K-means 聚類自動鎖定當前最佳 ATR 因子。
實戰多重濾網：包含空間緩衝區 (Buffer) 與信心門檻，大幅提升訊號品質。
📊 視覺說明
🚀 Rocket: AI confirms trend momentum.
⚡ Lightning: Trend exhaustion or reversal warning.
⚠️ Disclaimer: For educational and technical analysis purposes only.
Kevin J. Davey EURO Night StrategyEuro Night Strategy is a time‑filtered, volatility‑aware system originally built for Euro FX futures. Still, your adaptation to XAL and BTC on 1h bars makes sense because both markets show overnight drift patterns that the strategy can exploit.
Multiple Customizable MAs (SMA / EMA)Below is a simple code for multiple moving average lengths. Easy to use EMA/SMA
EMA 200 Color ChangeEMA 200 Color Change show the trend for price, if green with green shade then trend positive and if red with red shade then we assume trend is negative.
[Sumit Ingole] 200-EMA SUMIT INGOLE
Indicator Name: 200 EMA Strategy Pro
Overview
The 200-period Exponential Moving Average (EMA) is widely regarded as the "Golden Line" by professional traders and institutional investors. This indicator is a powerful tool designed to identify the long-term market trend and filter out short-term market noise.
By giving more weight to recent price data than a simple moving average, this EMA reacts more fluidly to market shifts while remaining a rock-solid trend confirmation tool.
Key Features
Trend Filter: Instantly distinguish between a Bull market and a Bear market.
Price above 200 EMA: Bullish Bias
Price below 200 EMA: Bearish Bias
Dynamic Support & Resistance: Acts as a psychological floor or ceiling where major institutions often place buy or sell orders.
Institutional Benchmark: Since many hedge funds and banks track this specific level, price reactions near the 200 EMA are often highly significant.
Reduced Lag: Optimized exponential calculation ensures you stay ahead of the curve compared to traditional lagging indicators.
How to Trade with 200 EMA
Trend Confirmation: Only look for "Buy" setups when the price is trading above the 200 EMA to ensure you are trading with the primary trend.
Mean Reversion: When the price stretches too far away from the 200 EMA, it often acts like a magnet, pulling the price back toward it.
The "Death Cross" & "Golden Cross": Use this in conjunction with shorter EMAs (like the 50 EMA) to identify major trend reversals.
Exit Strategy: Can be used as a trailing stop-loss for long-term positional trades.
Best Used On:
Timeframes: Daily (1D), 4-Hour (4H), and Weekly (1W) for maximum accuracy.
Assets: Highly effective for Stocks, Forex (Major pairs), and Crypto (BTC/ETH).
Disclaimer: This tool is for educational and analytical purposes only. Trading involves risk, and it is recommended to use this indicator alongside other technical analysis tools for better confirmation.
PineStats█ OVERVIEW
PineStats is a comprehensive statistical analysis library for Pine Script v6, providing 104 functions across 6 modules. Built for quantitative traders, researchers, and indicator developers who need professional-grade statistics without reinventing the wheel.
For building mean-reversion strategies, analyzing return distributions, measuring correlations, or testing for market regimes.
█ MODULES
CORE STATISTICS (20 functions)
• Central tendency: mean, median, WMA, EMA
• Dispersion: variance, stdev, MAD, range
• Standardization: z-score, robust z-score, normalize, percentile
• Distribution shape: skewness, kurtosis
PROBABILITY DISTRIBUTIONS (17 functions)
• Normal: PDF, CDF, inverse CDF (quantile function)
• Power-law: Hill estimator, MLE alpha, survival function
• Exponential: PDF, CDF, rate estimation
• Normality testing: Jarque-Bera test
ENTROPY (9 functions)
• Shannon entropy (information theory)
• Tsallis entropy (non-extensive, fat-tail sensitive)
• Permutation entropy (ordinal patterns)
• Approximate entropy (regularity measure)
• Entropy-based regime detection
PROBABILITY (21 functions)
• Win rates and expected value
• First passage time estimation
• TP/SL probability analysis
• Conditional probability and Bayes updates
• Streak and drawdown probabilities
REGRESSION (19 functions)
• Linear regression: slope, intercept, forecast
• Goodness of fit: R², adjusted R², standard error
• Statistical tests: t-statistic, p-value, significance
• Trend analysis: strength, angle, acceleration
• Quadratic regression
CORRELATION (18 functions)
• Pearson, Spearman, Kendall correlation
• Covariance, beta, alpha (Jensen's)
• Rolling correlation analysis
• Autocorrelation and cross-correlation
• Information ratio, tracking error
█ QUICK START
import HenriqueCentieiro/PineStats/1 as stats
// Z-score for mean reversion
z = stats.zscore(close, 20)
// Test if returns are normally distributed
returns = (close - close ) / close
isGaussian = stats.is_normal(returns, 100, 0.05)
// Regression channel
= stats.linreg_channel(close, 50, 2.0)
// Correlation with benchmark
spyReturns = request.security("SPY", timeframe.period, close/close - 1)
beta = stats.beta(returns, spyReturns, 60)
█ USE CASES
✓ Mean Reversion — z-scores, percentiles, Bollinger-style analysis
✓ Regime Detection — entropy measures, correlation regimes
✓ Risk Analysis — drawdown probability, VaR via quantiles
✓ Strategy Evaluation — expected value, win rates, R:R analysis
✓ Distribution Analysis — normality tests, fat-tail detection
✓ Multi-Asset — beta, alpha, correlation, relative strength
█ NOTES
• All functions return `na` on invalid inputs
• Designed for Pine Script v6
• Fully documented in the library header
• Part of the Pine ecosystem: PineStats, PineQuant, PineCriticality, PineWavelet
█ REFERENCES
• Abramowitz & Stegun — Normal CDF approximation
• Acklam's algorithm — Inverse normal CDF
• Hill estimator — Power-law tail estimation
• Tsallis statistics — Non-extensive entropy
Full documentation in the library header.
mean(src, length)
Calculates the arithmetic mean (simple moving average) over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Arithmetic mean of the last `length` values, or `na` if inputs invalid
wma_custom(src, length)
Calculates weighted moving average with linearly decreasing weights
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Weighted moving average, or `na` if inputs invalid
ema_custom(src, length)
Calculates exponential moving average
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Exponential moving average, or `na` if inputs invalid
median(src, length)
Calculates the median value over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Median value, or `na` if inputs invalid
variance(src, length)
Calculates population variance over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Population variance, or `na` if inputs invalid
stdev(src, length)
Calculates population standard deviation over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Population standard deviation, or `na` if inputs invalid
mad(src, length)
Calculates Median Absolute Deviation (MAD) - robust dispersion measure
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: MAD value, or `na` if inputs invalid
data_range(src, length)
Calculates the range (highest - lowest) over a lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Range value, or `na` if inputs invalid
zscore(src, length)
Calculates z-score (number of standard deviations from mean)
Parameters:
src (float) : Source series
length (simple int) : Lookback period for mean and stdev calculation (must be >= 2)
Returns: Z-score, or `na` if inputs invalid or stdev is zero
zscore_robust(src, length)
Calculates robust z-score using median and MAD (resistant to outliers)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Robust z-score, or `na` if inputs invalid or MAD is zero
normalize(src, length)
Normalizes value to range using min-max scaling
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Normalized value in , or `na` if inputs invalid or range is zero
percentile(src, length)
Calculates percentile rank of current value within lookback window
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Percentile rank (0 to 100), or `na` if inputs invalid
winsorize(src, length, lower_pct, upper_pct)
Winsorizes values by clamping to percentile bounds (reduces outlier impact)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
lower_pct (simple float) : Lower percentile bound (0-100, e.g., 5 for 5th percentile)
upper_pct (simple float) : Upper percentile bound (0-100, e.g., 95 for 95th percentile)
Returns: Winsorized value clamped to bounds
skewness(src, length)
Calculates sample skewness (measure of distribution asymmetry)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 3)
Returns: Skewness value (negative = left tail, positive = right tail), or `na` if invalid
kurtosis(src, length)
Calculates excess kurtosis (measure of distribution tail heaviness)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 4)
Returns: Excess kurtosis (>0 = heavy tails, <0 = light tails), or `na` if invalid
count_valid(src, length)
Counts non-na values in lookback window (useful for data quality checks)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Count of valid (non-na) values
sum(src, length)
Calculates sum over lookback period
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 1)
Returns: Sum of values, or `na` if inputs invalid
cumsum(src)
Calculates cumulative sum (running total from first bar)
Parameters:
src (float) : Source series
Returns: Cumulative sum
change(src, length)
Returns the change (difference) from n bars ago
Parameters:
src (float) : Source series
length (simple int) : Number of bars to look back (must be >= 1)
Returns: Current value minus value from `length` bars ago
roc(src, length)
Calculates Rate of Change (percentage change from n bars ago)
Parameters:
src (float) : Source series
length (simple int) : Number of bars to look back (must be >= 1)
Returns: Percentage change as decimal (0.05 = 5%), or `na` if invalid
normal_pdf_standard(x)
Calculates the standard normal probability density function (PDF)
Parameters:
x (float) : The value to evaluate
Returns: PDF value at x for standard normal N(0,1)
normal_pdf(x, mu, sigma)
Calculates the normal probability density function (PDF)
Parameters:
x (float) : The value to evaluate
mu (float) : Mean of the distribution (default: 0)
sigma (float) : Standard deviation (default: 1, must be > 0)
Returns: PDF value at x for normal N(mu, sigma²)
normal_cdf_standard(x)
Calculates the standard normal cumulative distribution function (CDF)
Parameters:
x (float) : The value to evaluate
Returns: Probability P(X <= x) for standard normal N(0,1)
@description Uses Abramowitz & Stegun approximation (formula 7.1.26), accurate to ~1.5e-7
normal_cdf(x, mu, sigma)
Calculates the normal cumulative distribution function (CDF)
Parameters:
x (float) : The value to evaluate
mu (float) : Mean of the distribution (default: 0)
sigma (float) : Standard deviation (default: 1, must be > 0)
Returns: Probability P(X <= x) for normal N(mu, sigma²)
normal_inv_standard(p)
Calculates the inverse standard normal CDF (quantile function)
Parameters:
p (float) : Probability value (must be in (0, 1))
Returns: x such that P(X <= x) = p for standard normal N(0,1)
@description Uses Acklam's algorithm, accurate to ~1.15e-9
normal_inv(p, mu, sigma)
Calculates the inverse normal CDF (quantile function)
Parameters:
p (float) : Probability value (must be in (0, 1))
mu (float) : Mean of the distribution
sigma (float) : Standard deviation (must be > 0)
Returns: x such that P(X <= x) = p for normal N(mu, sigma²)
power_law_alpha(src, length, tail_pct)
Estimates power-law exponent (alpha) using Hill estimator
Parameters:
src (float) : Source series (typically absolute returns or drawdowns)
length (simple int) : Lookback period (must be >= 10 for reliable estimates)
tail_pct (simple float) : Percentage of data to use for tail estimation (default: 0.1 = top 10%)
Returns: Estimated alpha (tail index), typically 2-4 for financial data
@description Alpha < 2 indicates infinite variance (very heavy tails)
@description Alpha < 3 indicates infinite kurtosis
@description Alpha > 4 suggests near-Gaussian behavior
power_law_alpha_mle(src, length, x_min)
Estimates power-law alpha using maximum likelihood (Clauset method)
Parameters:
src (float) : Source series (positive values expected)
length (simple int) : Lookback period (must be >= 20)
x_min (float) : Minimum threshold for power-law behavior
Returns: Estimated alpha using MLE
power_law_pdf(x, alpha, x_min)
Calculates power-law probability density (Pareto Type I)
Parameters:
x (float) : Value to evaluate (must be >= x_min)
alpha (float) : Power-law exponent (must be > 1)
x_min (float) : Minimum value / scale parameter (must be > 0)
Returns: PDF value
power_law_survival(x, alpha, x_min)
Calculates power-law survival function P(X > x)
Parameters:
x (float) : Value to evaluate (must be >= x_min)
alpha (float) : Power-law exponent (must be > 1)
x_min (float) : Minimum value / scale parameter (must be > 0)
Returns: Probability of exceeding x
power_law_ks(src, length, alpha, x_min)
Tests if data follows power-law using simplified Kolmogorov-Smirnov
Parameters:
src (float) : Source series
length (simple int) : Lookback period
alpha (float) : Estimated alpha from power_law_alpha()
x_min (float) : Threshold value
Returns: KS statistic (lower = better fit, typically < 0.1 for good fit)
is_power_law(src, length, tail_pct, ks_threshold)
Simple test if distribution appears to follow power-law
Parameters:
src (float) : Source series
length (simple int) : Lookback period
tail_pct (simple float) : Tail percentage for alpha estimation
ks_threshold (simple float) : Maximum KS statistic for acceptance (default: 0.1)
Returns: true if KS test suggests power-law fit
exp_pdf(x, lambda)
Calculates exponential probability density function
Parameters:
x (float) : Value to evaluate (must be >= 0)
lambda (float) : Rate parameter (must be > 0)
Returns: PDF value
exp_cdf(x, lambda)
Calculates exponential cumulative distribution function
Parameters:
x (float) : Value to evaluate (must be >= 0)
lambda (float) : Rate parameter (must be > 0)
Returns: Probability P(X <= x)
exp_lambda(src, length)
Estimates exponential rate parameter (lambda) using MLE
Parameters:
src (float) : Source series (positive values)
length (simple int) : Lookback period
Returns: Estimated lambda (1/mean)
jarque_bera(src, length)
Calculates Jarque-Bera test statistic for normality
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
Returns: JB statistic (higher = more deviation from normality)
@description Under normality, JB ~ chi-squared(2). JB > 6 suggests non-normality at 5% level
is_normal(src, length, significance)
Tests if distribution is approximately normal
Parameters:
src (float) : Source series
length (simple int) : Lookback period
significance (simple float) : Significance level (default: 0.05)
Returns: true if Jarque-Bera test does not reject normality
shannon_entropy(src, length, n_bins)
Calculates Shannon entropy from a probability distribution
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
n_bins (simple int) : Number of histogram bins for discretization (default: 10)
Returns: Shannon entropy in bits (log base 2)
@description Higher entropy = more randomness/uncertainty, lower = more predictability
shannon_entropy_norm(src, length, n_bins)
Calculates normalized Shannon entropy
Parameters:
src (float) : Source series
length (simple int) : Lookback period
n_bins (simple int) : Number of histogram bins
Returns: Normalized entropy where 0 = perfectly predictable, 1 = maximum randomness
tsallis_entropy(src, length, q, n_bins)
Calculates Tsallis entropy with q-parameter
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 10)
q (float) : Entropic index (q=1 recovers Shannon entropy)
n_bins (simple int) : Number of histogram bins
Returns: Tsallis entropy value
@description q < 1: emphasizes rare events (fat tails)
@description q = 1: equivalent to Shannon entropy
@description q > 1: emphasizes common events
optimal_q(src, length)
Estimates optimal q parameter from kurtosis
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Estimated q value that best captures the distribution's tail behavior
@description Uses relationship: q ≈ (5 + kurtosis) / (3 + kurtosis) for kurtosis > 0
tsallis_q_gaussian(x, q, beta)
Calculates Tsallis q-Gaussian probability density
Parameters:
x (float) : Value to evaluate
q (float) : Tsallis q parameter (must be < 3)
beta (float) : Width parameter (inverse temperature, must be > 0)
Returns: q-Gaussian PDF value
@description q=1 recovers standard Gaussian
permutation_entropy(src, length, order)
Calculates permutation entropy (ordinal pattern complexity)
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 20)
order (simple int) : Embedding dimension / pattern length (2-5, default: 3)
Returns: Normalized permutation entropy
@description Measures complexity of temporal ordering patterns
@description 0 = perfectly predictable sequence, 1 = random
approx_entropy(src, length, m, r)
Calculates Approximate Entropy (ApEn) - regularity measure
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 50)
m (simple int) : Embedding dimension (default: 2)
r (simple float) : Tolerance as fraction of stdev (default: 0.2)
Returns: Approximate entropy value (higher = more irregular/complex)
@description Lower ApEn indicates more self-similarity and predictability
entropy_regime(src, length, q, n_bins)
Detects market regime based on entropy level
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
q (float) : Tsallis q parameter (use optimal_q() or default 1.5)
n_bins (simple int) : Number of histogram bins
Returns: Regime indicator: -1 = trending (low entropy), 0 = transition, 1 = ranging (high entropy)
entropy_risk(src, length)
Calculates entropy-based risk indicator
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
Returns: Risk score where 1 = maximum divergence from Gaussian 1
hit_rate(src, length)
Calculates hit rate (probability of positive outcome) over lookback
Parameters:
src (float) : Source series (positive values count as hits)
length (simple int) : Lookback period
Returns: Hit rate as decimal
hit_rate_cond(condition, length)
Calculates hit rate for custom condition over lookback
Parameters:
condition (bool) : Boolean series (true = hit)
length (simple int) : Lookback period
Returns: Hit rate as decimal
expected_value(src, length)
Calculates expected value of a series
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Expected value (mean)
expected_value_trade(win_prob, take_profit, stop_loss)
Calculates expected value for a trade with TP and SL levels
Parameters:
win_prob (float) : Probability of hitting TP (0-1)
take_profit (float) : Take profit in price units or %
stop_loss (float) : Stop loss in price units or % (positive value)
Returns: Expected value per trade
@description EV = (win_prob * TP) - ((1 - win_prob) * SL)
breakeven_winrate(take_profit, stop_loss)
Calculates breakeven win rate for given TP/SL ratio
Parameters:
take_profit (float) : Take profit distance
stop_loss (float) : Stop loss distance
Returns: Required win rate for breakeven (EV = 0)
reward_risk_ratio(take_profit, stop_loss)
Calculates the reward-to-risk ratio
Parameters:
take_profit (float) : Take profit distance
stop_loss (float) : Stop loss distance
Returns: R:R ratio
fpt_probability(src, length, target, max_bars)
Estimates probability of price reaching target within N bars
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for volatility estimation
target (float) : Target move (in same units as src, e.g., % return)
max_bars (simple int) : Maximum bars to consider
Returns: Probability of reaching target within max_bars
@description Based on random walk with drift approximation
fpt_mean(src, length, target)
Estimates mean first passage time to target level
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for volatility estimation
target (float) : Target move
Returns: Expected number of bars to reach target (can be infinite)
fpt_historical(src, length, target)
Counts historical bars to reach target from each point
Parameters:
src (float) : Source series (typically price or returns)
length (simple int) : Lookback period
target (float) : Target move from each starting point
Returns: Array of first passage times (na if target not reached within lookback)
tp_probability(src, length, tp_distance, sl_distance)
Estimates probability of hitting TP before SL
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback for estimation
tp_distance (float) : Take profit distance (positive)
sl_distance (float) : Stop loss distance (positive)
Returns: Probability of TP being hit first
trade_probability(src, length, tp_pct, sl_pct)
Calculates complete trade probability and EV analysis
Parameters:
src (float) : Source series (typically returns)
length (simple int) : Lookback period
tp_pct (float) : Take profit percentage
sl_pct (float) : Stop loss percentage
Returns: Tuple:
cond_prob(condition_a, condition_b, length)
Calculates conditional probability P(B|A) from historical data
Parameters:
condition_a (bool) : Condition A (the given condition)
condition_b (bool) : Condition B (the outcome)
length (simple int) : Lookback period
Returns: P(B|A) = P(A and B) / P(A)
bayes_update(prior, likelihood, false_positive)
Updates probability using Bayes' theorem
Parameters:
prior (float) : Prior probability P(H)
likelihood (float) : P(E|H) - probability of evidence given hypothesis
false_positive (float) : P(E|~H) - probability of evidence given hypothesis is false
Returns: Posterior probability P(H|E)
streak_prob(win_rate, streak_length)
Calculates probability of N consecutive wins given win rate
Parameters:
win_rate (float) : Single-trade win probability
streak_length (simple int) : Number of consecutive wins
Returns: Probability of streak
losing_streak_prob(win_rate, streak_length)
Calculates probability of experiencing N consecutive losses
Parameters:
win_rate (float) : Single-trade win probability
streak_length (simple int) : Number of consecutive losses
Returns: Probability of losing streak
drawdown_prob(src, length, dd_threshold)
Estimates probability of drawdown exceeding threshold
Parameters:
src (float) : Source series (returns)
length (simple int) : Lookback period
dd_threshold (float) : Drawdown threshold (as positive decimal, e.g., 0.10 = 10%)
Returns: Historical probability of exceeding drawdown threshold
prob_to_odds(prob)
Calculates odds from probability
Parameters:
prob (float) : Probability (0-1)
Returns: Odds (prob / (1 - prob))
odds_to_prob(odds)
Calculates probability from odds
Parameters:
odds (float) : Odds ratio
Returns: Probability (0-1)
implied_prob(decimal_odds)
Calculates implied probability from decimal odds (betting)
Parameters:
decimal_odds (float) : Decimal odds (e.g., 2.5 means $2.50 return per $1 bet)
Returns: Implied probability
logit(prob)
Calculates log-odds (logit) from probability
Parameters:
prob (float) : Probability (must be in (0, 1))
Returns: Log-odds
inv_logit(log_odds)
Calculates probability from log-odds (inverse logit / sigmoid)
Parameters:
log_odds (float) : Log-odds value
Returns: Probability (0-1)
linreg_slope(src, length)
Calculates linear regression slope
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Slope coefficient (change per bar)
linreg_intercept(src, length)
Calculates linear regression intercept
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 2)
Returns: Intercept (predicted value at oldest bar in window)
linreg_value(src, length)
Calculates predicted value at current bar using linear regression
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Predicted value at current bar (end of regression line)
linreg_forecast(src, length, offset)
Forecasts value N bars ahead using linear regression
Parameters:
src (float) : Source series
length (simple int) : Lookback period for regression
offset (simple int) : Bars ahead to forecast (positive = future)
Returns: Forecasted value
linreg_channel(src, length, mult)
Calculates linear regression channel with bands
Parameters:
src (float) : Source series
length (simple int) : Lookback period
mult (simple float) : Standard deviation multiplier for bands
Returns: Tuple:
r_squared(src, length)
Calculates R-squared (coefficient of determination)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: R² value where 1 = perfect linear fit
adj_r_squared(src, length)
Calculates adjusted R-squared (accounts for sample size)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Adjusted R² value
std_error(src, length)
Calculates standard error of estimate (residual standard deviation)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Standard error
residual(src, length)
Calculates residual at current bar
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Residual (actual - predicted)
residuals(src, length)
Returns array of all residuals in lookback window
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Array of residuals
t_statistic(src, length)
Calculates t-statistic for slope coefficient
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: T-statistic (slope / standard error of slope)
slope_pvalue(src, length)
Approximates p-value for slope t-test (two-tailed)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Approximate p-value
is_significant(src, length, alpha)
Tests if regression slope is statistically significant
Parameters:
src (float) : Source series
length (simple int) : Lookback period
alpha (simple float) : Significance level (default: 0.05)
Returns: true if slope is significant at alpha level
trend_strength(src, length)
Calculates normalized trend strength based on R² and slope
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Trend strength where sign indicates direction
trend_angle(src, length)
Calculates trend angle in degrees
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Angle in degrees (positive = uptrend, negative = downtrend)
linreg_acceleration(src, length)
Calculates trend acceleration (second derivative)
Parameters:
src (float) : Source series
length (simple int) : Lookback period for each regression
Returns: Acceleration (change in slope)
linreg_deviation(src, length)
Calculates deviation from regression line in standard error units
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Deviation in standard error units (like z-score)
quadreg_coefficients(src, length)
Fits quadratic regression and returns coefficients
Parameters:
src (float) : Source series
length (simple int) : Lookback period (must be >= 4)
Returns: Tuple: for y = a*x² + b*x + c
quadreg_value(src, length)
Calculates quadratic regression value at current bar
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: Predicted value from quadratic fit
correlation(x, y, length)
Calculates Pearson correlation coefficient between two series
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Correlation coefficient
covariance(x, y, length)
Calculates sample covariance between two series
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 2)
Returns: Covariance value
beta(asset, benchmark, length)
Calculates beta coefficient (slope of regression of y on x)
Parameters:
asset (float) : Asset returns series
benchmark (float) : Benchmark returns series
length (simple int) : Lookback period
Returns: Beta coefficient
@description Beta = Cov(asset, benchmark) / Var(benchmark)
alpha(asset, benchmark, length, risk_free)
Calculates alpha (Jensen's alpha / intercept)
Parameters:
asset (float) : Asset returns series
benchmark (float) : Benchmark returns series
length (simple int) : Lookback period
risk_free (float) : Risk-free rate (default: 0)
Returns: Alpha value (excess return not explained by beta)
spearman(x, y, length)
Calculates Spearman rank correlation coefficient
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Spearman correlation
@description More robust to outliers than Pearson correlation
kendall_tau(x, y, length)
Calculates Kendall's tau rank correlation (simplified)
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period (must be >= 3)
Returns: Kendall's tau
correlation_change(x, y, length, change_period)
Calculates change in correlation over time
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period for correlation
change_period (simple int) : Period over which to measure change
Returns: Change in correlation
correlation_regime(x, y, length, ma_length)
Detects correlation regime based on level and stability
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period for correlation
ma_length (simple int) : Moving average length for smoothing
Returns: Regime: -1 = negative, 0 = uncorrelated, 1 = positive
correlation_stability(x, y, length, stability_length)
Calculates correlation stability (inverse of volatility)
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback for correlation
stability_length (simple int) : Lookback for stability calculation
Returns: Stability score where 1 = perfectly stable
relative_strength(asset, benchmark, length)
Calculates relative strength of asset vs benchmark
Parameters:
asset (float) : Asset price series
benchmark (float) : Benchmark price series
length (simple int) : Smoothing period
Returns: Relative strength ratio (normalized)
tracking_error(asset, benchmark, length)
Calculates tracking error (standard deviation of excess returns)
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
Returns: Tracking error (annualize by multiplying by sqrt(252) for daily data)
information_ratio(asset, benchmark, length)
Calculates information ratio (risk-adjusted excess return)
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
Returns: Information ratio
capture_ratio(asset, benchmark, length, up_capture)
Calculates up/down capture ratio
Parameters:
asset (float) : Asset returns
benchmark (float) : Benchmark returns
length (simple int) : Lookback period
up_capture (simple bool) : If true, calculate up capture; if false, down capture
Returns: Capture ratio
autocorrelation(src, length, lag)
Calculates autocorrelation at specified lag
Parameters:
src (float) : Source series
length (simple int) : Lookback period
lag (simple int) : Lag for autocorrelation (default: 1)
Returns: Autocorrelation at specified lag
partial_autocorr(src, length)
Calculates partial autocorrelation at lag 1
Parameters:
src (float) : Source series
length (simple int) : Lookback period
Returns: PACF at lag 1 (equals ACF at lag 1)
autocorr_test(src, length, max_lag)
Tests for significant autocorrelation (Ljung-Box inspired)
Parameters:
src (float) : Source series
length (simple int) : Lookback period
max_lag (simple int) : Maximum lag to test
Returns: Sum of squared autocorrelations (higher = more autocorrelation)
cross_correlation(x, y, length, lag)
Calculates cross-correlation at specified lag
Parameters:
x (float) : First series
y (float) : Second series (lagged)
length (simple int) : Lookback period
lag (simple int) : Lag to apply to y (positive = y leads x)
Returns: Cross-correlation at specified lag
cross_correlation_peak(x, y, length, max_lag)
Finds lag with maximum cross-correlation
Parameters:
x (float) : First series
y (float) : Second series
length (simple int) : Lookback period
max_lag (simple int) : Maximum lag to search (both directions)
Returns: Tuple:
Bias Daily 5.0Bias Daily Indicator with Breakout Alerts
Plots bullish, bearish, and consolidation bias levels based on previous daily candles. Alerts trigger when price breaks the previous daily high, low, or either, and bias lines show key levels on the chart.
5% D/ID or 15%W DropCan be used to trigger alerts for 5% daily drops or intra-day drops or 15% drops during the past 5 days. Useful for selling puts.
