Delivered in 5 weeks — weekly reporting time dropped from 6–8 hours to 30–45 minutes, and cross-team discrepancies dropped from 10–15% to under 0.5%.
A platform that enables the business to structure teams, assign managers, and collect financial results from each participant. Team members log profits, losses, trade volume, and other indicators in their own accounts. Managers see aggregated figures per team, period dynamics, and a breakdown by individual instruments. The main administrator oversees all teams at once, compares profitability, slices results across selected periods, and uses dashboards where every amount is automatically converted to the base currency using crypto exchange rates.
The client operates in the crypto space and runs several teams that generate profit through trading and adjacent operations. Before launch, data was scattered across exchange reports, spreadsheets, and managers' local notes. That made cross-team comparison and number verification hard. Crypto volatility introduced additional variability — the same return in BTC or USDT looked different depending on the calculation day. The client needed a single system where everyone worked by the same rules and the owner saw consolidated performance across the entire team portfolio.
Many teams and roles
Each team has a manager plus several participants, and the main admin must see all of them.
Different currencies and assets
Results are recorded in BTC, ETH, stablecoins, or fiat. Profitability had to be comparable in one base currency without manual recalculation.
Crypto volatility
Rates change every minute, so the system had to capture the quote at the time of each operation and use it correctly in period-based reporting.
Multi-level analytics
Managers needed only their own team slice, the owner needed the full cross-team picture with drill-down.
Different access scenarios
Minimal rights for participants, broader for managers, full for the administrator.
We built an analytics platform on Directus with a "team → manager → participants" hierarchy and built-in financial analytics. The backend owns data structure, crypto-rate sync, and result aggregation; the frontend handles role-specific cabinets and dashboards.
Data modeling
In Directus we defined entities for teams, users, roles, financial records, and reporting periods. Every participant is tied to a team and a role, and every operation auto-linked to the relevant level — personal, team, and global.
Quotes service
We integrated an external API for top-cap crypto rates. The system refreshes quotes on schedule, snapshots the rate at the moment a result is entered, and persists it — so reports are generated accurately for any past date.
Metric calculations
We computed all key indicators on the server (period profitability, team PnL, average business return, individual contribution). This ensures dashboards operate with pre-aggregated data and stay fast even on full-portfolio views.
Role-specific cabinets
We anticipated different views per role. A participant sees their own transactions plus the team summary; a manager sees every record in their team, period dynamics, and the core charts. For the admin we designed a separate mode with access to all teams.
Filters and slicing
In dashboards we implemented filtering by team, date range, asset, and operation type. The admin can pull "last week / month / quarter" for one team or compare several teams on one screen.
Roles, permissions, and audit
We configured the Directus role model so each user sees only their own data. Critical changes (e.g., editing already-entered results) are logged so disputed cases could be reviewed and resolved.
At the kickoff we defined how the client evaluates team performance: which indicators matter most, in which currency management decisions are made (USDT as base), and how often results are reviewed. We defined the operation types that must enter the system, the minimum field set, and the workflows for participants, managers, and admin.
The client got a single platform for working with crypto teams where every result is collected by the same rules and performance is directly comparable. Managers and the owner stopped wasting time on manual recalculations and stitching reports from different sources.
to prepare the weekly consolidated report — down from 6–8 hours
calculation discrepancies — down from 10–15%
for cross-team comparison — down from 2 hours
disputed cases in the first 2 months
the admin sees team performance and allocates resources from actual data, not screenshots
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