OPEN-SOURCE SCRIPT
Evo Market Decision Engine (EMDE)
Evo Market Decision Engine (EMDE) is an experimental market-reading tool designed to combine multiple layers of analysis into a single visual and decision-oriented indicator.
The idea behind this tool is simple: to provide a working framework capable of combining structure, flow, momentum, market regime, sentiment, multi-timeframe alignment, and probabilistic reading in order to produce a more synthetic view of the current market context.
The indicator includes:
- a kernel-smoothed price base
- price excess detection
- buyer / seller flow reading
- pivot-based structural analysis
- flow toxicity and market regime measures
- an adaptive scoring engine
- a multi-timeframe filter
- a simulated “footprint” reading block
- an NQ100 sentiment model based on major large-cap stocks
- a full dashboard with probability, bias, filters, and signals
The goal is not to provide an absolute truth, but to offer a decision-support engine capable of synthesizing multiple signals into a single analytical framework.
Important: I do not recommend following the positions or signals generated by this tool as-is.
I mainly used it as a working base, idea lab, and study framework, not as a ready-to-trade turnkey tool.
I am sharing it anyway because it may be useful for some of you, whether to understand the logic, study the code structure, modify certain blocks, or use it as a starting point to develop something else.
The source code is open, so everyone can do whatever they want with it: study it, adapt it, improve it, simplify it, or simply use it as inspiration.
The idea behind this tool is simple: to provide a working framework capable of combining structure, flow, momentum, market regime, sentiment, multi-timeframe alignment, and probabilistic reading in order to produce a more synthetic view of the current market context.
The indicator includes:
- a kernel-smoothed price base
- price excess detection
- buyer / seller flow reading
- pivot-based structural analysis
- flow toxicity and market regime measures
- an adaptive scoring engine
- a multi-timeframe filter
- a simulated “footprint” reading block
- an NQ100 sentiment model based on major large-cap stocks
- a full dashboard with probability, bias, filters, and signals
The goal is not to provide an absolute truth, but to offer a decision-support engine capable of synthesizing multiple signals into a single analytical framework.
Important: I do not recommend following the positions or signals generated by this tool as-is.
I mainly used it as a working base, idea lab, and study framework, not as a ready-to-trade turnkey tool.
I am sharing it anyway because it may be useful for some of you, whether to understand the logic, study the code structure, modify certain blocks, or use it as a starting point to develop something else.
The source code is open, so everyone can do whatever they want with it: study it, adapt it, improve it, simplify it, or simply use it as inspiration.
Skrip sumber terbuka
Dalam semangat TradingView sebenar, pencipta skrip ini telah menjadikannya sumber terbuka, jadi pedagang boleh menilai dan mengesahkan kefungsiannya. Terima kasih kepada penulis! Walaupuan anda boleh menggunakan secara percuma, ingat bahawa penerbitan semula kod ini tertakluk kepada Peraturan Dalaman.
Penafian
Maklumat dan penerbitan adalah tidak bertujuan, dan tidak membentuk, nasihat atau cadangan kewangan, pelaburan, dagangan atau jenis lain yang diberikan atau disahkan oleh TradingView. Baca lebih dalam Terma Penggunaan.
Skrip sumber terbuka
Dalam semangat TradingView sebenar, pencipta skrip ini telah menjadikannya sumber terbuka, jadi pedagang boleh menilai dan mengesahkan kefungsiannya. Terima kasih kepada penulis! Walaupuan anda boleh menggunakan secara percuma, ingat bahawa penerbitan semula kod ini tertakluk kepada Peraturan Dalaman.
Penafian
Maklumat dan penerbitan adalah tidak bertujuan, dan tidak membentuk, nasihat atau cadangan kewangan, pelaburan, dagangan atau jenis lain yang diberikan atau disahkan oleh TradingView. Baca lebih dalam Terma Penggunaan.