Basis Algorithmic Research

Trading algorithms for systematic market execution.

Research-driven execution systems across foreign exchange, digital assets, and event-driven market structure.

Approach

Research scope

Execution systems across three market structures

FX execution

Multi-timeframe systems for major pairs, built around directional context, liquidity zones, and confirmation logic before execution.

fu-strategy · vrvp-strategy · tinga-tinga

Digital asset systems

Perpetual futures and spot-market systems that test structural patterns in digital assets with defined sizing and exit controls.

bitcoin9to5 · tinga-tinga

Event-driven microstructure

Event-response systems for thin, fast markets, wrapped in risk checks that reject unacceptable instruments before execution.

event-feed

Strategies

Five systems with inspectable logic

Each strategy is implemented as running code with explicit signal rules, execution behavior, and risk controls.

FX execution

fu-strategy

Source

Maps higher-timeframe directional context and supply-demand zones, then evaluates lower-timeframe triggers before routing execution on the 1-minute chart.

  • Directional context and zone mapping on 4H and 1H
  • Real-time multi-timeframe confirmation
  • Automated execution on 1M
  • Operator alerts on signal events
PythonFastAPICapital.com

FX execution

vrvp-strategy

Source

Combines a 4H Supertrend filter with 1H Stochastic RSI, Fair Value Gaps, and Volume Profile to identify high-confluence execution windows.

  • Supertrend trend filter (4H)
  • Stochastic RSI momentum (1H)
  • Fair Value Gap and Volume Profile confluence
  • CLI backtest and simulation modes
PythonFastAPIVolume Profile

Cross-market execution

tinga-tinga

Source

An RSI-crossover system ported from MQL4, sized by account balance rather than fixed lots, with a backtester that reports win rate, profit factor, and drawdown.

  • RSI crossover entries
  • Balance-based position sizing
  • Binance API market data
  • Backtests: win rate, profit factor, Sharpe, drawdown
JavaScriptBinance APIBacktesting

Digital asset systems

bitcoin9to5

Source

Tests a time-of-day market structure thesis for BTC perpetual futures, alternating directional exposure between US cash-session hours and overnight windows.

  • Short 9:29am to 4:01pm ET, long overnight and weekends
  • BTC perpetual futures on Nado
  • Adaptive take-profit zone with trailing stop
  • Automatic direction changes at each session boundary
Digital AssetsNadoFutures

Event-driven microstructure

event-feed

Source

Detects token-liquidity migration events, applies a guardrail engine, and routes small test positions only when predefined risk checks pass.

  • Real-time migration-event detection
  • Guardrail checks for authority, liquidity, and holder concentration
  • Fast exit reaction under predefined target logic
  • Simulation default with hard circuit breakers
TypeScriptEvent FeedRisk Engine

Plus a Telegram signal-relay layer for SMS alerts (infrastructure, not a strategy).

Approach

How research becomes executable infrastructure

Every system follows the same four-stage pipeline, from market hypothesis to monitored operation.

  1. 01

    Signal detection

    Higher-timeframe context, supply-demand zones, trigger formation, trend filters, RSI crossovers, and event feeds are evaluated against the rules defined for each system.

  2. 02

    Multi-layer confirmation

    No single indicator trades alone. Stochastic RSI momentum, Fair Value Gaps, Volume Profile confluence, and instrument-quality checks filter entries before execution.

  3. 03

    Risk-managed execution

    Percentage-based targets, balance-based position sizing, adaptive trailing stops, and hard circuit breakers define the operating envelope for every system.

  4. 04

    Alerts & monitoring

    WhatsApp, SMS, and email notifications report trigger events, while status endpoints make strategy behavior observable during operation.

Risk disclaimer

Nothing on this page or in this code is financial advice. Automated trading carries a substantial risk of loss. Systems that performed well historically can still lose money in future market conditions. Past performance never guarantees future results, and all deployment decisions require independent review, controls, and accountability.