The trader must determine if the price is currently approaching a key Fibonacci zone or a recognized FMCBR floor/ceiling level. Step 2: Confirmation of Breakout
After the break, the trader does not enter immediately. Instead, they wait for the price to move back towards the broken level. This retest validates the strength of the new level. 3. Entering the Trade (CBR)
Understanding both the advantages and criticisms of the FMCBR indicator is crucial before deciding to use it. fmcbr indicator
To ensure the high probability of a trade, the FMCBR SOP (Standard Operating Procedure) checklist includes:
Monitor the asset until a candle decisively closes beyond the structural generated by the indicator. This confirms that the existing micro-trend has officially fractured, making a structural shift valid. Step 3: Wait for the Retest Zone Entry The trader must determine if the price is
If you want, I can: produce sample code for TradingView (Pine Script), Python backtest code, or suggest default parameters for a specific instrument and timeframe—tell me which.
The is a multi-layered technical analysis tool built primarily for the MetaTrader 4 (MT4) platform that automates the Fibo Musang Candle Breakout and Retest (FMCBR) trading strategy . Originally popularized within Southeast Asian retail trading communities, this indicator merges advanced multi-timeframe price action with precise Fibonacci retracement levels. It removes subjectivity from the charts by identifying Candle Breakout 1 (CB1) structural shifts, plotting key entry zones, and generating dynamic risk-to-reward setups. Core Mechanics of the FMCBR Indicator This retest validates the strength of the new level
def calculate_fmcbr(df, macd_fast=12, macd_slow=26, macd_signal=9, bb_period=20, bb_std=2): # MACD Histogram exp1 = df['close'].ewm(span=macd_fast, adjust=False).mean() exp2 = df['close'].ewm(span=macd_slow, adjust=False).mean() macd_line = exp1 - exp2 signal_line = macd_line.ewm(span=macd_signal, adjust=False).mean() macd_hist = macd_line - signal_line # Bollinger Bands sma = df['close'].rolling(window=bb_period).mean() std = df['close'].rolling(window=bb_period).std() bb_upper = sma + (bb_std * std) bb_lower = sma - (bb_std * std)