AI Developer for Trading Signal Bot

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1

I’m looking for an experienced AI/automation developer to build a real-time trading signal bot focused primarily on day trading.

The bot should analyze the market and identify high-quality trade setups using factors such as:

  • Price action and market structure
  • Volume
  • VWAP
  • Support and resistance
  • Moving averages
  • RSI and momentum
  • Breakouts/retests
  • Risk-to-reward
  • Overall trend

I want the system to grade setups (A+, A, B, C) and explain why it considers a setup strong or weak.

When a strong setup appears, the bot should send an alert with information such as:

Ticker: SPY
Direction: LONG
Setup Grade: A+
Entry: $XXX.XX
Stop Loss: $XXX.XX
Targets: $XXX.XX / $XXX.XX
Risk/Reward: 1:3
Reason: VWAP reclaim + volume confirmation + bullish market structure

I primarily watch SPY and QQQ, but I would like the ability to add additional stocks or ETFs.

Ideally, alerts could be delivered through Telegram, SMS, or email.

I already use TradingView, so experience integrating TradingView alerts/webhooks or market-data APIs would be helpful.

This is not a request for a simple indicator. I want a system that evaluates multiple signals together, filters out lower-quality trades, grades the opportunity, and provides an understandable explanation for each alert.

Please include:

  • Examples of similar trading/AI bots you’ve built
  • What technology/API you recommend
  • How you would obtain real-time market data
  • How you would design the A+ / A / B / C grading system
  • Estimated development time
  • Total project cost
  • Ongoing monthly costs for APIs/hosting
  • Your calendar/booking link so I can schedule a call with you
  • Please email me directly with your proposal and information

I’m looking for someone who understands both software development and active/day trading, not just someone who can build a basic TradingView indicator.

3

You’re not looking for another TradingView indicator, you need a real-time decision engine that filters noise, grades confluence, and explains exactly why a setup deserves an A+, A, B, or C.

I’d build this with TradingView/webhooks + Python + real-time market data (Polygon/Alpaca), then score VWAP, volume, structure, momentum, trend, breakout/retest quality, and R before sending alerts to Telegram/SMS/email.

For an SPY/QQQ MVP, I’d estimate 7–10 days, around $1,500–$2,000, with roughly $50–$200/month in data/hosting depending on the provider.

Before I lock in the architecture, do you want TradingView to remain the main signal source, or do you want the bot to evaluate the market independently from raw real-time data?

You can book a meeting with me here to discuss this further.

5

Hi @curtis3595,

Thanks for the detailed brief on the Make Community,
Day_Trading_Signal_Bot_Proposal_v2.pdf (14.8 KB)

I’ve attached a full proposal covering what I understand you want built, the recommended tech stack, how the A+ through C grading system would work, the development timeline, a full cost breakdown, and what I’d need from you to get started.

Short version: $3,400 fixed for the build (billed in two milestones), plus roughly $150-280/month in ongoing API and hosting costs billed directly to your own accounts.

Happy to walk through any of it on a call if that’s easier: Calendly

Taiwo
Pathfinder Automation Solutions

6

@curtis3595, I would keep version one paper-only and deterministic. The engine would calculate VWAP distance and reclaim, relative volume, market structure, trend, RSI or momentum, breakout and retest state, and planned R multiple. An LLM can explain a completed score in plain language, while the score and risk fields remain rule-based and testable.

A practical stack is TradingView webhooks into a small Python service, an event and decision log in SQLite or Postgres, then Telegram and email alerts. For broader raw-data coverage, Alpaca currently lists full US-exchange coverage at $99 per month. Hosting and monitoring should usually be about $15 to $40 per month for SPY and QQQ, with SMS usage separate.

I would grade only after hard checks for data freshness, session, spread, minimum planned reward-to-risk, and conflicting trend. The remaining weighted score would cover structure, VWAP, relative volume, momentum, breakout quality, and market alignment. Every alert would store its factor scores and the rule version so you can replay and audit it later.

TinyOps Studio has built event-driven monitors, webhook routing, scoring and alerting pipelines, audit logs, and automated test harnesses. I cannot truthfully claim a prior client day-trading bot. I can demonstrate fit with a small paper-data vertical slice before a larger build.

My proposed first milestone is $499 fixed: one instrument, one setup family, replayed or paper data, an explainable grade, Telegram or email alerting, and a test report. If accepted, I would credit that toward a $2,500 SPY and QQQ paper-trading MVP delivered in 10 business days. TinyOps works asynchronously in writing, so I do not offer a live sales call. If written scoping works for you, reply here or message TinyOps Studio with the setup family you want proven first.

Runnable synthetic proof: Paper trading signal proof - TinyOps Studio

11

Hi @curtis3595 — what you’re describing isn’t a trading bot, it’s a confluence scoring engine that grades opportunities and explains its reasoning, and that’s exactly the kind of system I build.

Straight up: I haven’t built a stock trading bot before. What I have built is a real-money sports-betting engine that does the identical job — it ingests live data, scores each opportunity across multiple weighted factors (my version uses expected-value and Monte Carlo simulation), filters out the low-quality plays, grades what’s left, and only fires an alert when something clears the bar, with a plain-English reason for why it’s a strong play. Swap “VWAP reclaim + volume confirmation” for my betting factors and it’s the same architecture.

How I’d approach yours:

  • Stack: TradingView webhooks → a Python decision service → Postgres for the event/decision log → Telegram + email/SMS alerts
  • Data: real-time via a market-data API (Polygon or Alpaca — I’d confirm which after we scope your exact instruments)
  • Grading: rule-based hard checks first (data freshness, session, spread, min R:R, conflicting trend), then a weighted score across structure/VWAP/relative-volume/momentum/breakout-quality/trend-alignment → maps to A+/A/B/C. Every alert stores its factor scores + rule version so you can replay and audit any signal later.
  • The explanation: the score stays rule-based and testable; the plain-English “why” is generated from the stored factors.

I’d want to prove fit before you commit to a full build — I’d rather show you a working vertical slice on one instrument (SPY, one setup family) with real explainable grading than ask you to trust a pitch.

Happy to put together a proper proposal with timeline and cost. What’s the best way to send it — you mentioned email but I don’t see it posted; want to DM it here, or drop your preferred contact?