Copy-Trading Signal System for Mobile Apps

TRUETECH is engaged in the development, support and maintenance of iOS, Android, PWA mobile applications. We have extensive experience and expertise in publishing mobile applications in popular markets like Google Play, App Store, Amazon, AppGallery and others.

Development and support of all types of mobile applications:

Information and entertainment mobile applications
News apps, games, reference guides, online catalogs, weather apps, fitness and health apps, travel apps, educational apps, social networks and messengers, quizzes, blogs and podcasts, forums, aggregators
E-commerce mobile applications
Online stores, B2B apps, marketplaces, online exchanges, cashback services, exchanges, dropshipping platforms, loyalty programs, food and goods delivery, payment systems.
Business process management mobile applications
CRM systems, ERP systems, project management, sales team tools, financial management, production management, logistics and delivery management, HR management, data monitoring systems
Electronic services mobile applications
Classified ads platforms, online schools, online cinemas, electronic service platforms, cashback platforms, video hosting, thematic portals, online booking and scheduling platforms, online trading platforms

These are just some of the types of mobile applications we work with, and each of them may have its own specific features and functionality, tailored to the specific needs and goals of the client.

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Copy-Trading Signal System for Mobile Apps
Complex
from 1 week to 3 months
Frequently Asked Questions

Our competencies:

Development stages

Latest works

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Copy-trading means that a master trader's deals are automatically copied to subscribers' accounts. Imagine: you find a trader with a 30% monthly return. You subscribe, but the signal arrives 10 seconds late — the price has already moved, the trade didn't happen. A mobile app must solve two fundamentally different tasks: for the master — publishing trades and managing subscribers, for the copier — receiving signals, setting copy parameters, and monitoring results. Our team, with 5+ years of experience in trading apps, has implemented over 20 copy-trading projects, and we know how to avoid such issues. Our experience guarantees deadlines and high quality.

How is a signal structured for copy-trading?

A signal is not just a notification "buy BTC". It's a structured object with enough context for automatic execution:

struct TradingSignal: Codable {
    let id: UUID
    let masterId: String
    let pair: String          // "BTC/USDT"
    let side: TradeSide       // .long, .short
    let entryPrice: Decimal?  // nil for market orders
    let takeProfit: [Decimal] // multiple TP levels
    let stopLoss: Decimal
    let leverage: Int?        // for futures
    let riskPercent: Decimal  // % of subscriber's deposit
    let comment: String?
    let publishedAt: Date
    let validUntil: Date?     // signal expires
}

riskPercent instead of a fixed amount — because subscribers have different deposits. 2% of $1,000 and 2% of $50,000 are different amounts but identical risk.

Signal delivery: latency matters

A signal to enter a position is time-critical. The price moves while the signal travels. Latency requirements depend on the master's strategy: for swing trading (positions held for days), push with a 5-10 second delay is sufficient. For scalping, WebSocket is mandatory — push is too slow. WebSocket, standardized protocol per RFC 6455, ensures latency <1 second, which is 10 times faster than push notifications — this is the key comparison for choosing the stack.

WebSocket for active users in the app. The backend broadcasts the signal to all connected subscribers of the master simultaneously.

FCM/APNs push for background notifications. An important nuance: push is not guaranteed delivery — FCM may buffer. For trading signals this is unacceptable. Therefore, when opening the app, always synchronize missed signals via GET /signals?since={lastReceivedAt}.

// Android — receiving and processing a signal
class SignalReceiver @Inject constructor(
    private val signalRepository: SignalRepository,
    private val orderExecutor: OrderExecutor,
    private val notificationManager: AppNotificationManager,
) {
    suspend fun handle(signal: TradingSignal) {
        signalRepository.save(signal)

        val subscription = signalRepository.getSubscription(signal.masterId) ?: return
        if (!subscription.autoExecute) {
            // Notification only, user decides manually
            notificationManager.showSignal(signal)
            return
        }

        // Auto-execution with checks
        val account = accountRepository.getCurrent()
        val orderSize = account.balance * subscription.riskPercent / 100
        if (orderSize < exchange.minimumOrderSize(signal.pair)) {
            notificationManager.showError("Insufficient balance to execute signal")
            return
        }

        val result = orderExecutor.execute(signal, orderSize)
        notificationManager.showExecutionResult(signal, result)
    }
}

How to configure copying parameters?

Each subscriber configures parameters separately for each master:

Position size:

  • Fixed % of deposit (follow master's risk)
  • Fixed amount in USDT
  • Multiplier of master's size (e.g., 0.5x if the master's volume is too large)

Signal filters:

  • Long only / Short only / Both directions
  • Pairs (allow only BTC, ETH; ignore altcoins)
  • Maximum leverage (do not copy if master uses > 10x)
  • Maximum number of simultaneous positions

Execution mode:

  1. Auto (immediately upon signal receipt)
  2. With confirmation (push → user taps "Execute")
  3. Notifications only (no execution, monitoring master's strategy)

The settings form is a key UX element. The user must understand the consequences of each parameter. For % of deposit — show calculation: "With a deposit of $2,000 and risk of 2% — $40 per trade."

Step-by-step guide to setting up copying:

  1. Select a master in the catalog.
  2. Set the risk percentage of deposit.
  3. Configure filters by pairs and leverage.
  4. Choose execution mode: auto, with confirmation, or notifications only.
  5. Confirm subscription.

Master screen: what the subscriber sees

The master's profile contains their trading metrics, not marketing text:

Metric Value
Win Rate 64%
Profit Factor 1.87
Max Drawdown −18.4%
Sharpe Ratio 1.31
Trades (30d) 142
Subscribers 2,840
Monthly PnL +12.3%

The master's equity curve is mandatory. Subscribers must see not just "+30% per year" but details: the presence of a prolonged drawdown plateau, a sharp rise at the beginning and stagnation afterward.

My results as a copier

A separate screen — PnL from copying a specific master. Data differs from the master's: different execution prices (slippage), different entry times (signal delivery delay), different position sizes.

Comparison: master's equity curve vs. my results on the same chart. If the discrepancy is large — analyze reasons (slippage, missed signals, filter limits).

// iOS, SwiftUI — comparing curves — Apple Documentation
Chart {
    ForEach(masterEquity) { point in
        LineMark(
            x: .value("Date", point.date),
            y: .value("PnL", point.pnl),
            series: .value("Type", "Master")
        )
        .foregroundStyle(.blue)
    }
    ForEach(myEquity) { point in
        LineMark(
            x: .value("Date", point.date),
            y: .value("PnL", point.pnl),
            series: .value("Type", "My Results")
        )
        .foregroundStyle(.green)
    }
}
.chartLegend(.visible)

Masters search and rating

Masters catalog with filters: by win rate, by drawdown, by number of trades, by activity period (minimum 3 months of active trading). Default sorting by Sharpe Ratio — the only metric that accounts for both return and risk simultaneously.

Master card in the list — compact: avatar, nickname, 3 key metrics, mini sparkline equity curve, "Subscribe" button.

What's included in the work

  • Signal delivery via WebSocket + FCM/APNs with missed signal synchronization
  • Copy settings form with filters and position size calculation
  • Master profile with metrics and equity curve
  • My results screen with comparison to master
  • Masters catalog with filtering and rating
  • Signal history: received, executed, missed (with reason)
More about signal filtering Signal filtering allows the subscriber to limit risk. For example, exclude trading altcoins with high volatility or limit maximum leverage. All filters are applied before execution, preventing unwanted trades.

Timeline

Role Features Timeline
Copier (subscriber) Signals, settings, results 10–14 days
Master Publishing, subscribers, analytics 7–10 days
Full system Catalog, ratings, both roles 20–28 days

Cost is calculated individually after requirements analysis. Copying profits can reach $500 per month, and savings on slippage up to $200 per year. Contact us to discuss your project — we implement the full cycle of signal system development. Request a demo: we will assess the architecture, timeline, and budget. Get a consultation on your project — we will evaluate the architecture and propose the optimal solution.

Mobile App Analytics: Firebase, Amplitude, AppsFlyer and Attribution

Our team regularly encounters projects where analytics is already "set up" but yields no real insights. A typical example is a startup with 50k DAU: tracking dozens of events without a single answer to the question "why don't users reach payment?". In two weeks we built a basic funnel and found that 70% of users drop off at the phone number verification screen. After fixing the bug, retention increased by 12%. The takeaway: analytics should start with specific questions, not tracking everything indiscriminately.

Why Event Taxonomy is the Foundation of Mobile App Analytics?

Firebase Analytics, Amplitude, Mixpanel — technically similar. The difference lies in what you put into them. A common mistake: events like screen_view, button_tap_1, button_tap_2 without context. A month later, no one remembers what button_tap_2 means.

Proper taxonomy: object + action + context. product_viewed, checkout_started, payment_completed with parameters product_id, category, price, source. This allows building funnels, cohort analysis, and retention without additional tracking.

We document the naming convention in a tracking plan — a document (Google Sheet or Amplitude Data Catalog) describing every event, its parameters, and triggering conditions. The tracking plan is synced with the analytics team before development begins, not after. This approach ensures that data remains interpretable months later and doesn't become a dump. Experience from 50+ projects confirms: without a tracking plan, analytics maintenance costs increase 2-3 times due to rework.

What Should You Choose for Mobile App Analytics: Firebase, Amplitude, or Mixpanel?

The table below highlights key differences between the three popular platforms. Choice depends on budget, traffic, and tasks.

Criteria Firebase Analytics Amplitude Mixpanel
Free limit Unlimited (Spark plan) Up to 10M events/month Up to 1K MTU/month (Special)
Data latency Up to 24 hours (standard) Minutes (real-time) Minutes (real-time)
Funnels and cohorts Basic funnels, limited count Deep funnels, Journeys, cohorts Funnels, Retention, Insights
BigQuery export Yes (free, raw data) Yes (subscription) Yes (Enterprise)
Session Replay No Yes (iOS/Android SDK) No
Ad integration Google Ads (native) Via Universal Links Via partners

Firebase Analytics — free, deep integration with Google Ads, BigQuery export for raw data. Limitations: data latency up to 24 hours, limited funnels. For startups with Google Ads traffic, it's the first choice.

Amplitude — product analytics focused on cohorts and user journeys. Journeys (formerly Pathfinder) shows actual paths between events — not assumed funnels but real routes. Session Replay records sessions for UX analysis. The free tier up to 10M events/month is enough for most products at launch.

Mixpanel — close to Amplitude, stronger in real-time segmentation. Insights, Funnels, Retention cover 90% of product analysts' tasks.

How to Solve Multi-Channel Attribution with AppsFlyer?

Knowing where a user came from is a separate task. Firebase Attribution works only within the Google ecosystem. For multi-channel attribution (Facebook Ads, TikTok, Apple Search Ads, programmatic), an MMP (Mobile Measurement Partner) is needed.

AppsFlyer is the market leader. OneLink — universal deep link working on iOS and Android, correctly attributing installs from any channel. Protect360 — built-in fraud protection (fake installs, click injection on Android). Adjust and Branch are competitors with similar features. Branch excels in deep linking; Adjust is popular in gaming.

According to Apple, with iOS 14.5, apps must obtain user permission via ATT before collecting IDFA for tracking. AppsFlyer uses probabilistic matching (IP + user agent + timing) for these users — accuracy is lower but better than nothing. SKAdNetwork and Privacy Preserving Attribution provide aggregated data from Apple with a 24-72 hour delay.

How to Set Up Crash Analytics to Not Miss Bugs?

Firebase Crashlytics is the standard for crash reporting. It automatically groups crashes by stack trace, shows affected users %, and sends velocity alerts when crash rate increases by more than 10% per hour.

Important: symbolication. On iOS, .dSYM files must be automatically uploaded with each build — via Fastlane upload_symbols_to_crashlytics or Xcode Cloud built-in. Without symbols, crashes in Crashlytics appear as memory addresses. This happens more often than expected when switching to a new CI — in one project with 500k users, we found that 40% of crashes remained unsymbolicated due to a missing CI/CD step. After automation, bug response time dropped from 3 hours to 15 minutes.

For React Native and Flutter, @sentry/react-native and sentry_flutter provide additional context: breadcrumbs, network requests before the crash, Redux/Provider state.

Below is a comparison of popular crash analytics tools to choose according to your needs.

Criteria Firebase Crashlytics Sentry Instabug
Free limit Unlimited (Spark) 5k events/month 250 MAU
Grouping By stack trace + parameters By fingerprint By stack trace + metadata
Symbolication Automatic (via file) Automatic (via CLI) Automatic
Velocity alerts Yes (by % change) Yes (by count) Yes (by threshold)
Extra context Logs, Keys, Custom Keys Breadcrumbs, User, Tags User steps, network requests
Price Free (in Firebase) Paid plans available Paid plans available

Environment Setup

Three environments with separate Firebase projects: dev, staging, production. Mixing analytics from test sessions and production is a common mistake that skews all metrics. On iOS via GoogleService-Info.plist per scheme, on Android via google-services.json in each flavor folder.

Timelines: basic analytics with Firebase + Crashlytics — 3-5 days. Full tracking plan + Amplitude/Mixpanel with funnels and cohorts — 2-3 weeks. Attribution via AppsFlyer with deep linking and fraud protection — 1-2 weeks. Cost is calculated individually based on integration complexity.

What Is Included in Our Work

As part of analytics implementation, we provide:

  • Development and approval of a tracking plan with product and marketing teams.
  • SDK integration (Firebase, Amplitude, Mixpanel, AppsFlyer) considering your stack (Swift/Kotlin/Flutter/React Native).
  • Setup of funnels, cohorts, dashboards, and alerts.
  • Automation of symbolication and .dSYM upload via Fastlane.
  • Documentation of events and parameters.
  • Team training on the analytics platform.
  • Two weeks of post-release support and tracking adjustments.

Our experience: 7 years of analytics implementation and over 80 successful projects in mobile development. We guarantee data correctness and transparency at every stage.

Contact us for a consultation on setting up analytics for your app. Request an audit of your current analytics — and we will show you which metrics you are losing.