Final Project: Building a Complete Algo Trading Application

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Final Assembly

Your build becomes a system

Now that we've covered Course Introduction and Setting Up Development Environment, market basics, data handling, API connectivity, strategy implementation, performance tuning, backtesting, and risk/security, you're ready to assemble the pieces into one coherent algo trading application.

This final project is not about adding a brand-new trading trick. It is about making the application behave like a real system:

data flows in predictably
strategy logic stays isolated
risk checks guard every order
execution is mode-aware: backtest, paper, or live
logs and metrics tell you what happened
secrets and controls are handled safely

Think of this lesson as your production-minded blueprint: small enough to build, structured enough to extend.

Final Assembly

System Architecture

The final project shape

A complete beginner-friendly but realistic layout looks like this:

Market/API Feed
      |
      v
Data Normalizer ---> Storage / Recent Window
      |
      v
Strategy Engine
      |
      v
Risk Gate
      |
      v
Execution Adapter
      |
      v
Broker / Exchange / Paper Simulator

Side channels:
- Config
- Logging
- Metrics
- Secrets
- Backtest runner

The key design principle: each layer has one job.

From Implementing Trading Algorithms, your strategy should not know API authentication details. From Risk Management and Security, your executor should not be able to send an order before the risk gate approves it.

This separation is what lets you test one piece at a time and replace paper trading with live execution later without rewriting the strategy.

System Architecture

Project Skeleton

Organize for speed and clarity

A clean project structure keeps the final application understandable as it grows:

algo_trader/
  CMakeLists.txt
  config/
    paper.json
    live.example.json
  include/
    market_data.hpp
    strategy.hpp
    risk.hpp
    execution.hpp
    metrics.hpp
  src/
    main.cpp
    strategy_sma.cpp
    risk_gate.cpp
    paper_executor.cpp
    api_connector.cpp
  tests/
    strategy_tests.cpp
    risk_tests.cpp
  logs/

For this final project, aim for these deliverables:

Backtest mode using historical data from earlier lessons
Paper mode using live-like data and simulated orders
Live-ready mode with execution disabled until explicitly configured
One strategy such as SMA crossover
One risk gate with position and loss limits
Basic metrics for latency and order decisions

You are building a foundation, not a hedge fund platform on day one.

Project Skeleton

Core Application

A minimal end-to-end trading loop

Below is a compact final-project prototype. It connects the layers you already learned:

tick data from Data Handling and Management in C++
SMA crossover from Implementing Trading Algorithms
position cap from Risk Management and Security
paper execution from Connectivity with Financial Market APIs concepts

It is intentionally small, but the boundaries are realistic.

```cpp
#include <cmath>
#include <deque>
#include <iomanip>
#include <iostream>
#include <numeric>
#include <optional>
#include <string>
#include <vector>

struct Tick {
    std::string symbol;
    double price;
};

struct Order {
    std::string symbol;
    std::string side;
    int qty;
    double price;
};

class SmaCrossoverStrategy {
public:
    SmaCrossoverStrategy(std::size_t fastWindow, std::size_t slowWindow)
        : fastWindow_(fastWindow), slowWindow_(slowWindow) {}

    std::optional<Order> onTick(const Tick& tick) {
        push(fast_, fastWindow_, tick.price);
        push(slow_, slowWindow_, tick.price);

        if (slow_.size() < slowWindow_) return std::nullopt;

        const double fastAvg = avg(fast_);
        const double slowAvg = avg(slow_);
        const int signal = fastAvg > slowAvg ? 1 : -1;

        // Avoid signal spam: only trade when direction changes.
        if (signal == lastSignal_) return std::nullopt;
        lastSignal_ = signal;

        return Order{tick.symbol, signal == 1 ? "BUY" : "SELL", 10, tick.price};
    }

private:
    std::size_t fastWindow_;
    std::size_t slowWindow_;
    std::deque<double> fast_;
    std::deque<double> slow_;
    int lastSignal_ = 0;

    static void push(std::deque<double>& q, std::size_t maxSize, double value) {
        q.push_back(value);
        if (q.size() > maxSize) q.pop_front();
    }

    static double avg(const std::deque<double>& q) {
        return std::accumulate(q.begin(), q.end(), 0.0) / q.size();
    }
};

class RiskGate {
public:
    explicit RiskGate(int maxAbsPosition) : maxAbsPosition_(maxAbsPosition) {}

    bool approve(const Order& order) const {
        const int signedQty = order.side == "BUY" ? order.qty : -order.qty;
        const int projected = position_ + signedQty;
        return std::abs(projected) <= maxAbsPosition_;
    }

    void applyFill(const Order& order) {
        position_ += order.side == "BUY" ? order.qty : -order.qty;
    }

    int position() const { return position_; }

private:
    int maxAbsPosition_;
    int position_ = 0;
};

class PaperExecutor {
public:
    void send(const Order& order) const {
        std::cout << "[PAPER EXECUTION] " << order.side << " "
                  << order.qty << " " << order.symbol
                  << " @ " << std::fixed << std::setprecision(2)
                  << order.price << '\n';
    }
};

int main() {
    std::vector<Tick> ticks = {
        {"ABC", 100.0}, {"ABC", 100.5}, {"ABC", 101.0},
        {"ABC", 100.7}, {"ABC", 100.2}, {"ABC", 99.8},
        {"ABC", 100.4}, {"ABC", 101.2}, {"ABC", 101.8}
    };

    SmaCrossoverStrategy strategy(2, 4);
    RiskGate risk(20);
    PaperExecutor executor;

    for (const auto& tick : ticks) {
        auto maybeOrder = strategy.onTick(tick);
        if (!maybeOrder) {
            std::cout << "No order for tick " << tick.symbol
                      << " price=" << tick.price << '\n';
            continue;
        }

        const Order& order = *maybeOrder;
        if (risk.approve(order)) {
            executor.send(order);
            risk.applyFill(order);
            std::cout << "Position now: " << risk.position() << "\n";
        } else {
            std::cout << "[RISK BLOCKED] " << order.side << " "
                      << order.qty << " would exceed position limit\n";
        }
    }

    std::cout << "Final position: " << risk.position() << '\n';
    return 0;
}
```

Boundary Check

In the final project architecture, which boundary is the safest and most maintainable?

Mode Matters

Before connecting real capital, the final project should run in _____ mode with live-like data and simulated orders.

Runtime Controls

Add operational brakes

A complete application needs more than strategy logic. It needs runtime controls that can stop trading quickly.

From Risk Management and Security, you already built the mental model: risk is not a report after the fact; it is a gate before execution.

This sample shows a simple control layer with:

application mode
maximum loss
maximum position
manual halt flag

In a real system, these values come from validated config, not random constants scattered through code.

```cpp
#include <cmath>
#include <iostream>
#include <string>

struct Order {
    std::string symbol;
    std::string side;
    int qty;
};

enum class Mode { Backtest, Paper, Live };

struct RuntimeState {
    Mode mode = Mode::Paper;
    bool manualHalt = false;
    double dailyPnl = 0.0;
    int position = 0;
};

class RuntimeRiskControl {
public:
    RuntimeRiskControl(double maxDailyLoss, int maxAbsPosition)
        : maxDailyLoss_(maxDailyLoss), maxAbsPosition_(maxAbsPosition) {}

    bool allow(const RuntimeState& state, const Order& order) const {
        if (state.manualHalt) {
            std::cout << "Blocked: manual halt is active\n";
            return false;
        }

        if (state.dailyPnl <= -maxDailyLoss_) {
            std::cout << "Blocked: daily loss limit reached\n";
            return false;
        }

        const int signedQty = order.side == "BUY" ? order.qty : -order.qty;
        const int projected = state.position + signedQty;
        if (std::abs(projected) > maxAbsPosition_) {
            std::cout << "Blocked: position limit would be exceeded\n";
            return false;
        }

        if (state.mode == Mode::Live) {
            std::cout << "Live mode detected: extra production confirmations required\n";
        }

        return true;
    }

private:
    double maxDailyLoss_;
    int maxAbsPosition_;
};

int main() {
    RuntimeRiskControl control(500.0, 30);
    RuntimeState state;
    state.mode = Mode::Paper;
    state.dailyPnl = -120.0;
    state.position = 25;

    Order buy{"ABC", "BUY", 10};
    Order sell{"ABC", "SELL", 10};

    std::cout << "Trying BUY order...\n";
    std::cout << (control.allow(state, buy) ? "Allowed\n" : "Rejected\n");

    std::cout << "Trying SELL order...\n";
    std::cout << (control.allow(state, sell) ? "Allowed\n" : "Rejected\n");

    state.manualHalt = true;
    std::cout << "Trying after manual halt...\n";
    std::cout << (control.allow(state, sell) ? "Allowed\n" : "Rejected\n");

    return 0;
}
```

Risk In Tests

True or false: The final project should bypass risk checks during backtesting so the strategy's raw performance looks clearer.

Observe Everything

Logs and metrics are part of the product

In Optimization and Performance Tuning, we measured before changing code. In the final application, measurement becomes permanent.

Track at least:

tick receive time
strategy decision time
risk approval time
execution adapter time
rejected order count
current position
active mode
connection state

You do not need a full monitoring stack to start. Even structured console logs and simple latency summaries can reveal bugs before they become expensive.

```cpp
#include <algorithm>
#include <chrono>
#include <iostream>
#include <numeric>
#include <string>
#include <vector>

class LatencyMetrics {
public:
    void record(const std::string& stage, long long micros) {
        samples_.push_back(micros);
        std::cout << "metric stage=" << stage
                  << " latency_us=" << micros << '\n';
    }

    void summary() const {
        if (samples_.empty()) return;

        const auto [minIt, maxIt] = std::minmax_element(samples_.begin(), samples_.end());
        const long long total = std::accumulate(samples_.begin(), samples_.end(), 0LL);
        const double avg = static_cast<double>(total) / samples_.size();

        std::cout << "--- latency summary ---\n";
        std::cout << "count=" << samples_.size() << '\n';
        std::cout << "min_us=" << *minIt << '\n';
        std::cout << "avg_us=" << avg << '\n';
        std::cout << "max_us=" << *maxIt << '\n';
    }

private:
    std::vector<long long> samples_;
};

int main() {
    LatencyMetrics metrics;

    // In a real app these would come from high_resolution_clock measurements.
    metrics.record("data_normalize", 35);
    metrics.record("strategy_decision", 52);
    metrics.record("risk_check", 11);
    metrics.record("paper_execute", 27);

    metrics.summary();

    std::cout << "Health: OK, mode=paper, connection=connected\n";
    return 0;
}
```

Launch Checklist

Your final project completion checklist

Before you consider the project complete, verify each item:

Correctness

Strategy output is deterministic on the same input
Backtest results include costs and slippage assumptions
Risk gate blocks oversized or unsafe orders
Paper executor records every simulated fill

Performance

Hot paths avoid unnecessary allocation
Latency is measured at key stages
Logs are useful but not excessive in the trading loop

Safety

Secrets are not committed
Live mode requires explicit configuration
Manual halt works
Reconnect behavior is tested

Maintainability

Strategy, risk, data, and execution are separate modules
Config is validated at startup
Errors are logged with enough context
Tests cover strategy and risk behavior

If these are true, you have not just written C++ code — you have built an algo trading application foundation.

Launch Checklist

Course Wrap

You now have the full path

You started by setting up a C++ workspace and learning how to measure early. Then you moved through market vocabulary, data structures, API connectivity, trading algorithms, optimization, backtesting, and risk/security.

The final project ties them together into this loop:

Receive data -> Normalize -> Decide -> Check risk -> Execute -> Measure -> Improve

Your next best step is practical:

Build the project skeleton.
Implement the paper-mode loop first.
Add one strategy and one risk gate.
Backtest it.
Run it in paper mode for many sessions.
Review logs and metrics.
Only then think about live execution.

C++ gives you the control and performance needed for millisecond-sensitive systems, but disciplined architecture is what makes that speed usable. Congratulations — you now have a complete roadmap for building your own algo trading application.

Course Wrap

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