Django CRM

As part of enhancing the cybersecurity level of our infrastructure, we need to abandon the practice of storing "eternal" and static API keys, passwords, and integration tokens in the configuration files (.env, appsettings.json, config.yaml) of our microservices. Business Goal: Create a single secure storage point for confidential data (secrets) with a mechanism for their automatic updating (rotation) in external systems on a schedule. Our other services will request current tokens "on the fly" via API, which will minimize damage in case of compromise of any system component.Security Model and Encryption (Crypto Core) No secret should be stored in plaintext in the database. Upon application startup, a Master Key is passed to the environment variables. If the key is missing or has an invalid length, the service should fail at the initialization stage with a clear error in the logs. Each secret is encrypted before being written to the database using this Master Key. Upon request, it is decrypted in memory and returned in the response body.Audit Logging (Audit Trail) Any action with secrets (creation, reading by the service, successful or unsuccessful rotation) must be recorded in a separate log file audit.log (or a separate table in the database). Strict Taboo: It is strictly prohibited to record the actual values of secrets in the audit log (neither in plaintext nor in encrypted form).

Need a specialist for writing parsers who can bypass CLOUDFRAME. Parsing of products occurs from sites with authorization. There are 10+ donors of varying complexity, with different levels of protection. Parsing of products occurs from sites with authorization. Parses data into a ready-made Mysql database + photographs on the server. It is necessary to write a parser according to the tasks described in the technical assignment and adapt the data to the existing database for full functionality on the site. Technical assignment and example donor upon request. Desktop parsers and C# are not considered.

Bot for mirroring positions on Binance Futures (Python) A bot is needed that reads my positions on Hyperliquid (public API) and Bitget Futures (my read-only key) and proportionally replicates them on my Binance USDT-M Futures via API. Logic: opening, increasing, partial closing, full closing — everything is mirrored with a customizable size ratio. Polling every 5–10 seconds. Correct handling of partial closures and averaging is mandatory. Requirements: notifications in Telegram about trades and errors; config (pairs, ratio, limits); deployment on my VPS + instructions; source code is transferred to me. I will enter the keys myself. Stages: 1) Hyperliquid→Binance, test with small amounts; 2) Bitget→Binance. Payment through safe in stages. In your response, indicate your experience with exchange APIs and how you will handle a partial closure of 30% of the position by the leader.

A deep technical verification of three PDF files for authenticity and possible signs of editing or forgery is required. Not only a visual assessment of the documents is needed. The performer must have a good understanding of the internal structure of PDF files and be able to analyze: file metadata; PDF structure and individual objects; creation history and possible editing; software used; embedded fonts, images, layers, and other elements; possible signs of re-saving, conversion, modifications, or backdating of the document; any technical discrepancies that may indicate manipulation of the files. Based on the verification results, a clear written conclusion regarding each file must be provided, indicating the identified signs, risks, and limitations of the verification. We are considering specialists who have practical experience in digital forensics, PDF document analysis, metadata, or verifying electronic files for authenticity. In your response, please briefly describe your experience, methods, and tools that you use for such verification.

The indicator for use in trading should determine certain ranges with history and analyze them with the current one.