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Ammar Tahir's avatar
Ammar Tahir
Sahiwal, Pakistan
UI/UX Designer crafting intuitive, modern digital experience
New to Contra
UI/UX Designer crafting intuitive, modern digital experience
Cover image for Banking Dashboard UI Design
Banking Dashboard UI Design
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2
Cover image for Fintech Mobile App UI/UX Design
Fintech Mobile App UI/UX Design
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3
Cover image for Crypto Trading App UI Design
Crypto Trading App UI Design
0
1
Cover image for Personal Finance Mobile App UI/UX Design
Personal Finance Mobile App UI/UX Design
0
1
TradingView(7)
Raj Kumar's avatar
Raj Kumar
Chennai, India
Algo Trading Developer - Pine Script,MQL5 &Python Automation
43
Followers
Algo Trading Developer - Pine Script,MQL5 &Python Automation
Cover image for Designed and implemented a robust
Designed and implemented a robust webhook-based trading automation system that connects TradingView signals to real-time trade execution via the Tradovate API. The system is built using Python and handles signal reception, validation, and order execution in a seamless automated pipeline. The architecture ensures low-latency communication, secure data handling, and reliable execution of trades without manual intervention. It processes incoming webhook alerts, applies validation logic, and triggers precise API-based trade execution. Key features include: • Real-time webhook signal processing (TradingView alerts) • Python-based signal receiver and validation engine • Secure and reliable API integration (Tradovate) • Low-latency execution pipeline • Fully automated trade workflow (zero manual input) • Scalable design for multi-strategy integration This solution is ideal for traders and firms looking to automate their strategies using webhook-driven architecture, enabling faster execution, reduced human error, and consistent performance in live trading environments.
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214
Cover image for Developed a fully automated trading
Developed a fully automated trading system for MetaTrader 5 (MT5) that executes trades based on validated multi-indicator signals. The system integrates TradingView-generated signals with a Python-based processing engine, which evaluates multiple technical conditions before triggering precise trade execution. The Expert Advisor (EA) is designed with advanced trade logic, risk management, and real-time execution capabilities. It ensures consistent performance by removing emotional decision-making and strictly following predefined rules. Key features include: • Automated trade execution on MT5 (buy/sell orders) • Integration with external signal sources (TradingView via webhook) • Multi-indicator validation (RSI, EMA, ADX, Volume, etc.) • Built-in risk management (lot sizing, SL/TP control) • Real-time processing with minimal latency • Fully customizable strategy logic This system enables traders to automate their strategies end-to-end — from signal generation to execution — ensuring speed, accuracy, and consistency in live market conditions.
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166
Cover image for Engineered a high-performance Python-based trading
Engineered a high-performance Python-based trading signal engine designed to generate precise and reliable trade signals using multi-indicator confluence logic. The system evaluates over 12 technical indicators including RSI, EMA, ADX, Volume, and trend-based conditions to identify strong market opportunities while effectively filtering out noise and false signals. The architecture is built to process real-time market data, validate multiple conditions simultaneously, and trigger only high-probability trade setups. This significantly improves signal accuracy and reduces unnecessary trades. Key capabilities include: • Multi-indicator confluence engine (12+ conditions) • Real-time signal processing and validation • False signal reduction using layered logic • Scalable architecture for integration with trading APIs • Seamless compatibility with automated execution systems (webhooks, APIs) This system can be integrated with platforms like TradingView, Tradovate, or custom trading environments to enable fully automated trading workflows — from signal generation to execution. Designed for traders and firms looking to achieve consistent, rule-based, and emotion-free trading through intelligent automation.
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184
Cover image for Built a complete end-to-end automated
Built a complete end-to-end automated trading system that captures alerts from TradingView, processes them through a Python-based webhook engine, validates multi-condition logic, and executes trades directly via the Tradovate API — eliminating manual intervention and improving execution speed and accuracy.
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166
TradingView(4)
Arifa Zanom's avatar
Arifa Zanom
proDhaka, Bangladesh
Killer Pitch Deck and Presentation Designer
$10k+
Earned
1x
Hired
11
Followers
Killer Pitch Deck and Presentation Designer
Cover image for MIOO-Pitch Deck
MIOO-Pitch Deck
0
1
Cover image for One Pager Flyer Design
One Pager Flyer Design
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0
Cover image for Novant - One pager Flyer
Novant - One pager Flyer
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2
Cover image for Real Estate Pitch Deck
Real Estate Pitch Deck
0
5
TradingView(4)
Prem Patel's avatar
Prem Patel
proIndia
Make.com Level 5 & Zapier Expert: AI and Ops Automation
10
Followers
Make.com Level 5 & Zapier Expert: AI and Ops Automation
Cover image for How do you get TradingView
How do you get TradingView alerts into Telegram in under 2 seconds, 24/7, without losing or doubling a signal? The pipeline is easy to describe and hard to keep alive. Four traps decide it. The shape: TradingView fires a webhook, Make.com catches it, formats the message the way the channel wants it, and posts it. Capture to post in under 2 seconds, around the clock, no human in the loop. That is the scenario in the image, running for a registered research analyst since 2025. 1. The 3-second window. TradingView gives a webhook receiver about 3 seconds to respond. A slow receiver loses the alert and nothing anywhere tells you. Fix: acknowledge instantly, process afterwards, never inline. 2. Telegram's rate cap. A channel takes roughly 20 messages a minute. One volatile candle across several symbols can fire more than that, and the extras vanish silently. Fix: a per-channel queue that preserves order and releases under the cap. 3. Duplicates. Retries and repeated triggers post the same signal twice unless every alert carries a dedupe key. Two identical entries in a row is how a channel loses trust in one afternoon. Fix: a key built from symbol, signal and bar time, checked before every send. 4. Silent death. The worst failure is the pipeline stopping while you assume it is posting. Fix: every run logged, every failure alerting you on a separate channel within seconds, and a heartbeat so silence itself raises an alarm. One more for Indian channels: if the channel belongs to a SEBI-registered adviser, promotional messages need the registration number leading the message and you need retrievable send records. That is a formatting and logging problem, so the pipeline handles it. Branded alert images instead of plain text are the same pipeline with one extra step: an image engine lays the signal over a base card by URL, so every alert ships as a picture. That build is its own case study on my profile. The full case study is on my profile, and the delivered version (I install it on your Make.com account, with your templates, queue, dedupe, alerting and a send log) is the TradingView product on my services page. Prem Patel, Nex Automations. Make.com Level 5 certified, official Make partner, Zapier Certified Expert.
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329
Cover image for TradingView alerts to Telegram in under 2 seconds on Make.com
TradingView alerts to Telegram in under 2 seconds on Make.com
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30
Cover image for Does someone on your team
Does someone on your team lose a day or two building the sales report every week or month? That was the starting point at Mokobara, the premium travel brand. Pulling sales across all their stores, checking returns, reconciling the numbers and emailing each manager took 1 to 2 days every cycle, with 8+ hours of that spent just reconciling. I built them an automation on Make.com (http://Make.com) a few months ago. It has run on its own ever since, weekly and monthly: Make pulls every sales record from BigQuery, 100K to 250K per cycle. That is too many for one request, so it reads them page by page and stitches them back together. It calculates the numbers the team actually uses: net sales per store after returns and discounts, return rates and the change against the last period. It builds a CSV with the full breakdown and a short email summary you can read in 60 seconds. It reads a Google Sheet of store representatives and emails every one of them the report for their own store. The result: the reporting problem is gone. The report went from 1 to 2 days of manual work to fully automatic, and the 8+ hours of reconciliation dropped to zero. Two things I would do the same way again: Calculate "net sales" in the automation, not in the warehouse. The business rule for what counts as a net sale is not what the raw data stores. Send people their slice, not the whole report. One report for everyone gets skimmed by everyone. The figures in the image are placeholders, the real ones stay with the client. What report is your team still building by hand? Tell me where the data lives and I will tell you how I would automate it.
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Cover image for Is your Contra discovery score
Is your Contra discovery score stuck? Mine went from top 45% to top 20% in 13 days, and posting more is not what moved it. And i need real Project On Contra😅 I log my score on every tag with a small script that reads all 48 options on the discovery page, so this is what actually changed between 27 August and 9 September 2026, and what did not. What moved it: Case studies. From 0 passing Contra's quality check to 15. Contra's checklist is short: a custom cover, a clear descriptive title, an engaging description, in-depth content and real media. My first two projects failed it, one on the description and one on the media. Services. From 1 passing service to 9, each with its own cover, a descriptive title, a rate and a real summary. Every tag a service carries gets the lift, so tag the tools you actually used. Profile quality, 66% to 100% after the profile rewrite. Replying to the first inquiry quickly. Response time was blank until then. It now reads 2 hours. The result: Overall top 45% to top 20%. Automation Engineer top 25% to top 10%. Make top 35% to top 20%. What did not move it: the four days after. From 9 to 13 September every tag gained exactly 1 point. Nothing new on the profile, nothing moves. And the three biggest levers on my board are still at zero: earnings, reviews and verified projects. All three come from the same move, one paid project routed through Contra, so that is where my next week goes. One thing if you compare yourself with others: Contra's point scale changed between my runs, so compare your rank band (top X%) rather than the raw points. What moved yours? If yours is stuck, reply with your score and the tag, and I will tell you which lever I would pull first.
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225
TradingView(2)
Adeyi Olamide's avatar
Adeyi Olamide
proLagos, Nigeria
Product designer and No code Developer
5.0
Rating
3
Followers
Product designer and No code Developer
Cover image for Rin Fintech App
Rin Fintech App
0
2
Cover image for MOVA — Multi-Currency Finance Experience
MOVA — Multi-Currency Finance Experience
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0
Cover image for Vyralix
Vyralix
0
2
Cover image for PulseFit
PulseFit
0
2
TradingView(1)
János Kiss's avatar
János Kiss
Budapest, Hungary
Flutterflow Pro | Fast, Beautiful, Business-Ready Apps
1x
Hired
5.0
Rating
14
Followers
Flutterflow Pro | Fast, Beautiful, Business-Ready Apps
Cover image for AI-Powered Trading Indicator Development for TradingView
AI-Powered Trading Indicator Development for TradingView
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37
Cover image for Tudatosság Klub - Meditation App built in Flutterflow
Tudatosság Klub - Meditation App built in Flutterflow
1
25
Cover image for Educational App Rebuild for Doctors
Educational App Rebuild for Doctors
0
11
Cover image for UX Design for Persuva's AI-Powered Landing Page Generator
UX Design for Persuva's AI-Powered Landing Page Generator
0
6
TradingView(1)