Python / Network Engineer: Network optimization of the bot (WAF, Connection Pooling, Asyncio)

Hello, I am a Quantitative Software Engineer with an academic background in high-energy physics. My main specialization is the development of low-level trading infrastructure, R&D, and creating fast execution routers.
Although I focus on infrastructure in my portfolio, developing MEV strategies is intrinsically linked to reverse engineering. Therefore, conducting R&D on your target platform, deobfuscating the JS bundle, and extracting the mathematics for generating dynamic tokens to transfer into fast Python code is a clear task for me in the research phase.
I will respond briefly and directly to your requirements:

1. Experience in optimizing network requests: I developed a production MEV bot for liquidations on Arbitrum. In Web2 terms, this is a bot for automatic sniping with an e2e reaction budget of ~30-100ms (where the Internal Overhead of the engine itself was less than 1 ms, the rest being the physical network latency of public nodes).
Regarding the requirements specified in your terms of reference: I will implement a pool of hot connections with a background pulse through a reusable session manager, completely eliminating the costs of new TLS handshakes. I will solve the floating start task you described through a strict mathematical phase shift; sockets will poll the server with an ideal comb (as you mentioned). To enhance this logic, I will apply micro-jitter to the timers, which will hide the zero dispersion of intervals and protect against bans by behavioral filters of WAF for blind machine spam.

2. TLS fingerprint spoofing:
Standard SSL in Python will reveal the bot with its JA3 fingerprint. I will integrate a client based on BoringSSL (via curl_cffi or Rust). A hard configuration (GREASE signatures, the required cipher order) will make the traffic cryptographically indistinguishable from legitimate Chrome.
…
3. Cryptography without I/O blocking:
I have written the mathematics of elliptic curves (secp256k1/ECDSA) and SHA256 from scratch. To ensure that the call to your C library does not paralyze the Event Loop, I will write a zero-cost wrapper in Rust (PyO3) using the GIL-release pattern (py.allow_threads). All heavy mathematics will run in a background OS thread, not stalling the Radar for even a microsecond.

Timeline: up to 2-3 weeks
Cost: From $800 (I will provide the exact figure after assessing the header files of your library and target KPIs)
Acceptance: I fully agree to a strict binding to Internal Overhead.

If I have piqued your interest, I would be happy to share details of my experience and the solutions I would use.

Show original

Translate

Good day.

I am responding consciously: I understand that this is not about "another parser on requests + sleep," but about an isolated network engine with measurable internal overhead on Linux. The basic business logic in Python is already in place — I am ready to integrate and optimize the transport layer without breaking the existing JSON/XML assembly and authorization. NDA — okay.

1. Experience in optimizing network requests

I have worked with high-frequency and latency-sensitive scenarios on asyncio:

Connection pooling + Keep-Alive: one TCPConnector / custom pool with a limit per host, force_close=False, session reuse, control of ttl_dns_cache, connect/read timeouts separately.
… Warming up the pool: a background coroutine with a legal heartbeat (HEAD/OPTIONS/light GET on the same hosts), so as not to pay TCP + TLS handshake for each shot before the "combat" window.
Removing Event Loop locks: heavy cryptography (digital signatures, RSA/ECDSA) — only through asyncio.to_thread / run_in_executor, a queue of signatures with batching, without synchronous calls in the hot path.
Rate limits: token bucket / leaky bucket at the engine level, jitter, staggered phase polling instead of blind spam; metrics p50/p95/p99 RTT and "time from trigger to packet send."
Diagnostics: uvloop (where appropriate), profiling loop lag, logging slow callbacks.
I do not propose a proxy on the combat shot — I agree with your condition (the server is already in the target location).

2. TLS fingerprint / WAF in Python (how I would solve it)

The goal is not "magical bypass," but a ClientHello/JA3 agreed with the browser, so the load balancer does not cut based on the fingerprint.

Practical stack:

curl_cffi (libcurl-impersonate) — modes impersonate="chrome120" / safari, etc.: a real browser TLS+HTTP/2 fingerprint without manual fuss with OpenSSL.
Alternative: tls-client / wrappers under the same principle.
Pure aiohttp + ssl.SSLContext — only if the WAF is weak; on aggressive platforms, this is usually insufficient, as JA3/ALPN/H2 settings do not match Chrome.
Additionally (if the WAF looks at the HTTP layer):

order of headers, User-Agent / sec-ch-ua agreed with the impersonate profile;
HTTP/2 where the platform expects it;
a single profile for the entire pool of connections, without a "zoo" of fingerprints.
Verification: comparison of JA3 with a reference browser (ja3er / local sniff) + testing on staging before acceptance based on your overhead metrics.

3. Approach to tasks from the technical specification

Network engine (separate module):
NetworkEngine — warm pool, heartbeat, fire(request) without handshake on the hot path, metrics connect/reuse/blocked_time.

Staggered radar:
N workers with a phase shift phase_i = base + i.

Show original

Translate

Hello.

I am responding because I understand what is being discussed; I have been in parsing for many years and understand the nuances of socket connections (previously programmed in C++/Win API/TCP/UDP).

My last completed project involved Go parsing and monitoring of foreign classifieds (Vinted, Jofogas, Basoz, OLX) + Telegram bot.

I have reviewed the project description. I have experience in developing asynchronous services in Python (asyncio, aiohttp/httpx), working with HTTP connections, profiling latencies, and optimizing high-frequency requests.

From the description, I see several areas for analysis:
… - the operation of the connection pool and Keep-Alive;
- time losses on TCP/TLS reconnections;
- blocking of the Event Loop during signing operations;
- polling strategy within existing limits.

Before discussing the final effect, I would like to understand the current architecture of the project itself:
- overall how it works, what the project is, its code, documentation if available;
- what HTTP client is currently being used;
- where exactly the main time losses are recorded;
- are there profiling results or measurements for the stages of the request;
- what exactly is meant by TLS Fingerprinting within the project.

After reviewing the current implementation, I will be able to propose a work plan, expected effect, and a reasonable estimate.

I work on an hourly basis, starting from 25 USD/hour.

Show original

Translate

Good day!

I have practical experience in developing high-load asynchronous services in Python using asyncio, aiohttp, and FastAPI, as well as optimizing network interactions, working with connection pools, and reducing internal execution delays.

I have worked on projects where response speed, effective use of the Event Loop, minimizing overhead, and stable operation under high load are critical.

I also have relevant projects in my portfolio: Freelancehunt

From the task description, I understand the main bottlenecks of the project: network delays, connection reuse, interaction with cryptographic modules, optimization of asynchronous execution, and control of internal system overheads.
…
I am ready to sign an NDA and familiarize myself in detail with the current architecture. After analyzing the existing implementation, I will be able to propose a specific optimization plan, identify potential performance growth points, and agree on acceptance criteria.

For a substantive discussion, please provide additional details on the current stack, libraries used, and target performance metrics.

I suggest discussing timelines, costs, and technical details in private correspondence after reviewing the project.

Show original

Translate

Good day!
I understand the task. A high-performance network engine is needed for a sniping bot with strict requirements for minimal latency, bypassing WAF, and maintaining stability under load.
I specialize in such tasks — optimizing critical network paths in a competitive environment.
Answers to your questions:
Experience in optimizing network requests
I have experience creating high-load snipers and HFT-like systems. I have optimized several bots where every millisecond was critical. Main techniques: connection pooling with keep-alive, session reuse, fine-tuning TCP sockets, custom HTTP client based on aiohttp + asyncio, controlling concurrency limits, jitter + staggered requests.
How I would solve the task of replacing TLS fingerprints
I would use curl_cffi (the best option available today) or httpx + tls-client with real browser fingerprints (Chrome 124+, Edge, Firefox).
Additionally:
… I generate unique JA3 / HTTP2 fingerprints for each stream
Rotation of Client Hello parameters
Custom SSLContext with controlled cipher suites and extensions
Imitation of real browser HTTP headers + order
This allows passing even quite aggressive WAFs.
Deadline: 10–14 calendar days (including testing on your infrastructure)
Fully isolated network module (AsyncClient)
Connection Pooling + Keep-Alive + Warm-up
TLS Fingerprinting + Anti-WAF protection
Staggered Polling with adaptive jitter
Integration of cryptography through run_in_executor without blocking the Event Loop
Detailed documentation + test stand
I am ready to sign an NDA and start within 1–2 days after agreeing on the details.
I look forward to your response and additional technical details (which specific endpoints, what WAF restrictions you are currently observing).
Best regards,

Show original

Translate

Hello! I have relevant experience with similar tasks, and I can help with your project. I can quickly familiarize myself with the requirements, propose the best solution, and execute the work meticulously. I am ready to discuss the details and get started.

Show original

Translate

Hello! I am the manager of FullStack developer Valentin. We specialize in high-load systems, automation, and SaaS development. Valentin has carefully reviewed your technical specifications. Here are his technical responses to your questions:

Experience in optimization: He has worked with HFT and custom parsers. He knows how to squeeze milliseconds. To maintain the "hot" pool, we will write a custom connection manager that will send lightweight dummy requests (heartbeat) in the background so that the WAF does not drop the session.

Bypassing WAF (TLS): Using raw aiohttp or requests here is pointless — JA3/JA4 fingerprints will immediately reveal the bot. We will use curl_cffi (a loop with assembly for Chrome/Firefox formatting) or build a custom SSL context through ctypes / cython if native Python process speed is important without overhead from C calls.

Non-blocking cryptography: Moving signing to ProcessPoolExecutor (as others suggest) incurs overhead on data serialization between processes (IPC), which will kill the speed of sniping. Valentin suggests implementing signing through a non-blocking loop.run_in_executor based on ThreadPoolExecutor (if the signing library releases GIL) or writing a microservice in Go/C++ purely for signature generation, communicating with the main engine via Unix Domain Sockets (UDS). This will provide minimal Internal Overhead.

Regarding the conditions: We understand the criticality of speed metrics. We are ready to sign an NDA. We will provide exact timelines and costs (approximately within your budget) immediately after you specify the target metrics for Internal Overhead in private correspondence.
…
Approximately: 3–5 days.
Budget: $180 – $220 (depending on the strictness of your target metrics for Internal Overhead, which we will agree upon).

Valentin is ready to join the discussion on architecture. When would it be convenient for you to discuss in detail?

Show original

Translate

Hello! The task is hardcore and requires an understanding of I/O and networking in Linux at a low level. My main profile is backend development in Python (FastAPI, asynchronous architecture, Docker) and extensive work with Linux environments, so I am very familiar with the specifics of network optimization.

I will immediately answer your questions from the technical specifications:

1. Experience in optimizing network requests (Connection Pooling):
To maintain a pool of "hot" connections, it is advisable to use aiohttp.TCPConnector with fixed limits (limit_per_host) and active Keep-Alive. This will prevent wasting precious time on TCP/TLS handshakes during a live request.

2. TLS fingerprint spoofing to bypass WAF:
Standard modules (ssl, aiohttp, requests) will be banned due to default JA3 fingerprints. To bypass aggressive WAFs (for example, Cloudflare), I use the curl_cffi library (or tls-client). It allows simulating TLS signatures at the C library level, making requests indistinguishable from legitimate traffic from Chrome or Edge.
…
3. Integrating cryptography without blocking the Event Loop:
Since signing a digital signature is purely a computational (CPU-bound) task, it will freeze the asynchronous radar. It needs to be delegated to a separate thread pool via asyncio.to_thread or concurrent.futures.ThreadPoolExecutor.

Show original

Translate

Hello! Having reviewed your project, I am ready to start working on it. I can offer optimal solutions to achieve the best results.

Show original

Translate

To minimize delays of the sniper bot on Ubuntu, it is critical to eliminate the overhead of TCP/TLS handshakes and not block the Event Loop with computations. I understand your problem: the WAF drops inactive sessions, and local packet signing slows down event processing. I will set up a persistent pool of Keep-Alive connections with background pinging and organize staggered polling with phase offsets to bypass platform limits without getting banned.
I have experience optimizing HFT and sniping systems. For spoofing TLS fingerprints (JA3/JA4), I use curl_cffi or custom SSL contexts in aiohttp with cipher overrides. Heavy cryptographic operations will be moved to ProcessPoolExecutor to prevent them from causing freezes in the main thread. Here is an example of non-blocking sending:
signature = await loop.run_in_executor(executor, sign_packet, payload)
response = await custom_pool.post(url, data=signature)
I am ready to start working as soon as you provide a description of the target metrics for Internal Overhead.

Show original

Translate