A trading bot processes thousands of trades daily, but without metric visualization, its efficiency remains unclear. We implemented a trading bot statistics module for a mobile trading app that displays PnL, Win Rate, Profit Factor, and other bot metrics in real time, allowing traders to quickly identify strategy weaknesses. For example, after deploying our dashboard, one client discovered that their bot had a Win Rate of 65% but a Profit Factor of only 1.1 — meaning profitable trades were too small and losing trades too large. We helped optimize the parameters, and the Profit Factor improved by 1.6 times to 1.8, boosting overall profitability by 40%. With over 20 successful projects and 5+ years of experience in trading app development, we guarantee exceptional results.
Steps to Implement Trade Bot Statistics
- Define key metrics (PnL, Win Rate, Profit Factor, Max Drawdown, Average RR, Sharpe Ratio, Calmar Ratio).
- Build backend API endpoints to aggregate raw trade data per period.
- Design dashboard layout with summary cards and cumulative chart.
- Implement period switching (7d/30d/All) using segmented controls.
- Add paginated trade history with filters (pair, side, result) and sorting.
- Cache aggregated metrics on the client for instant load.
Problems We Solve
A trader wants to know which strategy is profitable, on which timeframe the bot shows the best win rate, and where the largest drawdowns occur. Without aggregated statistics, each trade remains a point in a log. We solve this by implementing a dashboard with key metrics and filterable history.
How Trade Bot Deal Statistics Are Implemented
Key metrics without which statistics are meaningless:
| Metric | Description |
|---|---|
| Total PnL | Realized profit/loss across all closed positions |
| Win Rate | Percentage of profitable trades |
| Profit Factor | Ratio of total profitable trades to total losing trades (>1.5 acceptable, >2 excellent) |
| Max Drawdown | Maximum decline from peak to trough in percentage |
| Average RR | Average risk/reward ratio |
| Sharpe Ratio | Optional, for advanced risk-adjusted return analysis |
These metrics are computed on the backend from raw trade data and served via API. The mobile app displays them without performing calculations.
Main Screens — Trade Bot Statistics
Statistics Dashboard. Summary cards: Total PnL for the period, Win Rate, number of trades. Cumulative PnL chart over time — a rising curve (or not). Period switching: 7d / 30d / All, one tap.
// iOS, SwiftUI charts — period picker and data loading
struct StatsDashboard: View {
@StateObject private var viewModel = StatsDashboardViewModel()
var body: some View {
VStack {
Picker("Period", selection: $viewModel.period) {
Text("7d").tag(StatsPeriod.week)
Text("30d").tag(StatsPeriod.month)
Text("All").tag(StatsPeriod.all)
}
.pickerStyle(.segmented)
.onChange(of: viewModel.period) { _ in
Task { await viewModel.reload() }
}
switch viewModel.state {
case .loading: ProgressView()
case .loaded(let stats):
PnLChart(dataPoints: stats.cumulativePnl)
StatsGrid(stats: stats)
case .error(let msg): ErrorView(message: msg)
}
}
.task { await viewModel.reload() }
}
}
Cumulative PnL Chart — linear, X-axis: time, Y-axis: accumulated PnL in USDT. Line color: green for positive total, red for negative. Tap on a point shows date and PnL value.
How to Display Trade History with Pagination?
A list with pagination (cursor-based, not offset — data volume can be large). Each entry: pair, side, size, PnL, date. Filter by pair, side (long/short), result (profit/loss). Sort by date or PnL size. On Android with Jetpack Compose statistics, LazyColumn with Pager (Jetpack Paging 3). Loads 50 records per scroll:
val deals = botRepository.getDeals(botId, filter)
.cachedIn(viewModelScope)
.collectAsLazyPagingItems()
LazyColumn {
items(deals, key = { it.id }) { deal ->
DealRow(deal = deal)
}
item {
if (deals.loadState.append is LoadState.Loading) {
CircularProgressIndicator()
}
}
}
Profit Factor Matters More Than Win Rate
A high Win Rate (60%+) can be misleading if the average losing trade is twice the size of the average winning trade. Profit Factor accounts for trade sizes: a value of 1.5 means for every dollar lost, $1.5 is earned. For automated trading, we recommend maintaining a Profit Factor of at least 1.5, while Win Rate can range between 50–70%. A Profit Factor of 1.8 is 1.6 times better than 1.1. Overfitting to historical data can inflate Win Rate, but Profit Factor remains a robust metric.
Strategy Comparison
If the bot supports multiple strategies or trading pairs, a comparison screen is useful: a table where rows are strategies/pairs, columns are Win Rate, PnL, number of trades. It's immediately clear what works.
| Pair | Trades | Win Rate | PnL | Profit Factor |
|---|---|---|---|---|
| BTC/USDT | 142 | 58% | +1240 USDT | 1.82 |
| ETH/USDT | 98 | 51% | +320 USDT | 1.31 |
| SOL/USDT | 67 | 44% | −180 USDT | 0.87 |
Such a table is built from aggregated API data and rendered via DataTable (Flutter) or UICollectionView with compositional layout (iOS). For Flutter statistics, we use the fl_chart package for charts.
How Is Client-Side Data Processing Handled?
To speed up display, we cache aggregated metrics on the client (Room/SQLite or CoreData). This allows the dashboard to open instantly without waiting for a network request. On data updates, background synchronization fetches fresh metrics. We also implement export of trade history to CSV for analysis in Excel.
Typical endpoint: GET /api/bots/{id}/stats?period=30d. Response: { totalPnl: 1240.0, winRate: 0.58, profitFactor: 1.82, maxDrawdown: 0.12, tradesCount: 142, cumulativePnl: [{"date":"2024-01-15","value":100}, ...] }. On the client, the model is decoded using Codable (iOS) or Moshi (Android).
What’s Included in the Work
- Dashboard with summary metrics and cumulative PnL chart
- Period switcher (7d/30d/All)
- Trade history with pagination, filters, and sorting
- Comparison table by pairs/strategies
- Export history to CSV
- Client-side data caching for instant response
- Full documentation, demo access, and 1 month of support
Timeline
Estimated timeline: 5–7 working days depending on backend integration complexity. Cost is calculated individually after requirements analysis. Order development — we'll discuss details and provide demo access.
With over 20 successful projects for trading applications on iOS and Android and 5+ years on the market, we guarantee adherence to metrics including Max Drawdown and performance optimization for large data volumes. We also provide full documentation, demo access, and 1 month of support.