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Cryptocurrency Trading Simulation and Analytics Platform

HomeProjectsCrypto Trading Simulation
Analytics dashboard with simulated trades, results per token and time patterns

Webzenz Switzerland developed a platform that simulates TradingView strategies via webhooks, records around 900 trades a day in MongoDB and evaluates strategies with data.

Overview

Before a trading strategy is used with real capital, it should be tested under real market conditions. This platform receives signals from TradingView strategies written in Pine Script on USDT pairs via webhook and records them as simulated trades.

Around 900 simulated trades are created per day, about 6300 per week. The data is stored in MongoDB and analysed.

Starting point

With hundreds of signals a day, manual analysis is not possible. The client needed a solution that records signals reliably and derives clear insights about tokens, time windows and strategies.

Project at a glance

Project
Crypto Trading Simulation
Category
Fintech Application
Project type
Simulation and analytics platform
Industry
Fintech, cryptocurrencies
Hosting
AWS, Tokyo region
Delivered by
Webzenz Switzerland, our Swiss partner

Services

  • Data analytics
  • Web development
  • Python development

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What was built

Features and delivery

Webhook intake

TradingView sends signals to the platform via POST.

Simulated trades

Every signal is recorded as a simulated trade without real capital.

Analysis by token

Results per token show which pairs suit a strategy.

Time patterns

Analysis by time of day and weekday.

Strategy comparison and trends

Efficiency of different strategies compared and over time.

Role of Webzenz Switzerland

Scope of work

  • Concept and development of the platform
  • Webhook interface and data model
  • Data analysis with pandas and NumPy
  • Hosting and deployment on AWS

Webzenz Switzerland is responsible for the technical delivery. Strategies, parameters and trading decisions remain with the client. This reference describes software and is not investment advice.

Technology stack

Technologies used

Backend and API
  • Python
  • Flask
  • Jinja2
  • Flask-Login
  • Flask-Cors
  • gunicorn
  • schedule
  • requests
  • secure-smtplib
  • Telethon
Frontend
  • CSS
  • JavaScript
Trading and market data
  • TradingView Webhooks
  • Pine Script
  • ccxt
  • unicorn-binance-websocket-api
Database and data layer
  • MongoDB
  • Flask-PyMongo
  • pymongo
  • python-bsonjs
  • dnspython
  • pandas
  • NumPy
Servers, cloud and hosting
  • AWS Elastic Beanstalk
  • AWS EC2
  • AWS CodePipeline
  • Route 53
  • AWS IAM
  • AWS ACM
  • CloudFront
  • AWS Tokyo (ap-northeast-1)
Outcome

Result

The client can compare strategies on large amounts of data before making decisions. The analyses show which combinations of token, time window and strategy hold up.

Projects

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View project: Passive Brain
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  • Python
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  • AWS Lambda
  • EC2
View project: Live Price Websocket

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