Knowledge-base AI chatbots, Telegram bots, parsers, integrations and web services — built to spec, with live demos and open code you can try before paying. I also built and run my own 24/7 trading infrastructure — so "runs in production" is experience, not a slogan.
Few things are harder than a normal web service: real-time exchange streams, risk, execution, running 24/7 unattended. I built that solo and keep it in production. It's the guarantee that your bot, parser or service also reaches stable production — not just a demo.
Production-hardened integrations: Binance futures & options streams, Deribit chains and DVOL pipelines, Polymarket CLOB (EIP-712 signing, on-chain CTF redemptions, proxy networking), Hyperliquid, MOEX brokers.
rate-limit-aware clients · time-sync singletons · reconnect disciplineMicroservices (API / market-data / trading engine) over Redis Streams IPC, TimescaleDB + PgBouncer, uvloop, Prometheus metrics, property-based tests for state machines, watchdog daemons for ~20 strategy runners, alerting with dedupe.
FastAPI · Socket.IO · Svelte 5 front · documented incident post-mortemsPre-trade risk gate (notional / positions / daily-loss caps with HTTP killswitch), centralized capital book preventing cross-strategy over-commitment, market-maker quoting engine with inventory management.
no hardcoded prices · economic (realized + unrealized) accounting onlyA 6-gate anti-overfit validation pipeline: tiered costs, Deflated Sharpe, Bonferroni, out-of-sample hold-out, parameter plateaus, walk-forward. Honest fill models — paper fills only on real market crossing, adverse selection measured via markout-to-fair.
Monte-Carlo risk-of-ruin · Kelly sizing · execution-cost auditsOver 200 strategy candidates tested, ~18 research arcs closed with written kill verdicts, 2 structural edges validated 6/6 and run in paper-forward before any capital. A naive paper fill model once showed +30% per round-trip; the honest adverse-selection model killed it in 4 days of forward data. That discipline is exactly what protects production capital — yours included.
I build and license the engine, you bring the strategy. Define your own rules, validate them against an anti-overfit pipeline, then run them paper or live — self-hosted, on your keys, under your control. No signals, no managed money.
Express entry, exit and sizing logic in a clean Python interface — or hand me a spec and I wire it in. The framework handles the plumbing: data, state, scheduling, recovery.
your rules · your parameters · no black boxRun it through the same 6-gate validation that guards my own capital: tiered costs, Deflated Sharpe, out-of-sample hold-out, walk-forward, honest fills. See if the edge is real before a cent is at stake.
Monte-Carlo risk-of-ruin · Kelly sizing · paper-forwardPlug into the venues you trade — CEX REST/WS, options, perps, on-chain — with a pre-trade risk gate, capital limits and an HTTP killswitch built in.
rate-limit-aware · reconnect discipline · economic accountingRuns on your infrastructure with your API keys. No custody of your funds, no shared wallet, no lock-in. You own the deployment and every line of the config.
your keys · your servers · your dataStrategy as parameters — symbols, timeframe, entry/exit rules, risk limits. No edits to the engine core.
Run on history, then re-run out-of-sample. If the edge lives only in-sample it's overfit — and you see it here, not with capital.
Real market, real latency, zero real money. The bot runs for days against the live book until behaviour matches the backtest.
demo modeOnly after paper holds up. Your API keys — trade-only, no withdrawal — self-hosted, killswitch ready.
This is what you get — the honest out-of-sample number, not the backtest number. Per-asset optimization made the rule look ~3× stronger in-sample, yet out-of-sample it underperformed a strategy with no tuning at all (a 33.8 pp decay). The validation flags the overfit before any capital is committed — which is the entire point of running it.
You describe the strategy and venues; I deliver a tested, documented, deployable system. Fixed-scope or retainer, paid in stablecoins.
License the connectors / backtest / execution toolkit and get it running for your own strategies, with onboarding and support.
I sell technology, not returns. You configure, test and run your own strategies and you own the risk. I make no performance promises and never take custody of your capital — this is engineering, not investment advice or asset management.
A public, dependency-free repo with three reference strategy archetypes, one honest backtest engine and the same in-sample → out-of-sample check I run on my own capital. Real public BTC/USDT daily data, no keys, no network. These are teaching artifacts — not profitable bots and not the proprietary strategies — built to show method and code quality, not to promise returns.
Long while a fast moving average sits above a slow one; flat otherwise.
Buy when price is stretched below its own mean; exit as it reverts.
Enter on a new N-day high; exit on an M-day low.
Every tuned "winner" decays out-of-sample — that gap is the entire point. The flattering backtest number is not the number you get; the honest out-of-sample one is. browse the demo repo →
Not only trading. These ship with a live bot and open code you can try on the page before paying anything (case write-ups in Russian).
Answers from your documents with source citations and an honest "I don't know" — no hallucinations. Live chat + open code on the page.
Menu, step-by-step lead capture with manager alerts, knowledge-base answers. Live bot right on the page.
We built and run a production CRM (Ladero): multi-tenant, billing, messengers. Proof we ship full products to production.
A product I'm weighing — tell me if you'd use it. A no-code builder where you assemble a bot from blocks instead of Python, and it runs your idea through the same overfit-validation you saw above, then shows you honestly whether the edge survives out-of-sample — before a cent is at risk. Most strategies don't survive; a serious tool tells you so up front instead of selling you a flattering backtest.
Pick an archetype, set entry/exit and sizing with forms — no Python, no install. The engine handles data, state and execution.
Every bot auto-runs in-sample → out-of-sample plus risk-of-ruin. If it's overfit you see it in red — the tool's job is to talk you out of bad bots.
Runs on your infrastructure and trade-only keys, or testnet. No custody of funds, no signals, no managed money, no profit promises.
This does not exist yet — and I won't pretend otherwise. I'm measuring whether enough people want an honest no-code bot tool before building it. Join the waitlist and I'll reach out when there's something real to try. No payment, no obligation, no spam.
Best fit: AI chatbots and Telegram bots, parsers and integrations, web services and automation — plus trading infrastructure and honest strategy validation. Payment in USDC/USDT (any chain) or wire. Code samples and architecture deep-dives available on request.
Ballpark pricing: Telegram bot from 25 000 ₽ · knowledge-base AI chatbot from 50 000 ₽ · landing from 30 000 ₽ · integration/automation from 20 000 ₽ · web service / SaaS MVP from 150 000 ₽. Exact price after a short scoping chat; first engagement via escrow, free discovery before you pay.
A short call or async thread to size the work and see if it's a fit. No charge, no commitment to proceed.
Clone a public sample bot and run it yourself in paper mode — inspect the code and the IS → OOS validation before paying anything. demo repo →
One strategy — backtested, paper-deployed, fixed small fee, held in escrow. Judge the real work before committing to a full build.
Send the scope in the form below — what you need built and where.
We scope & quote async — fixed-scope or retainer, with a clear deliverable.
Build & deliver — tested, documented, on your infra or a private repo.
Payment: USDC / USDT (any chain), escrow for first engagements, or bank wire.
Fixed-scope billed by milestone · retainers monthly. No custody of your funds.