Traders spend hours manually scanning for graphical patterns. Algorithmic recognition can handle thousands of instruments in seconds. We develop an automatic pattern recognition system for crypto trading that detects head and shoulders, triangles, and wedges in real time and sends alerts. Our system processes data from any exchange and suits both short-term and long-term strategies. Unlike standard Pine Script indicators, our engine uses advanced extremum filtering and multi-threaded processing, allowing us to scan 500+ instruments on the 4H timeframe in 15–30 seconds. Order development for your tasks — we will evaluate your project and propose an optimal solution.
The head and shoulders pattern is easy to spot on a static chart. But detecting it algorithmically across thousands of instruments is a different story. Our ten years of experience in blockchain development and pattern recognition allows us to build robust detectors that work in production. Automation of analysis reduces manual effort, providing significant cost savings.
How the detection algorithm works
The core approach is to identify local extrema (pivot points) and analyze their sequence. The algorithm scans OHLCV data, finds swing highs and swing lows, then matches configurations against reference templates. For each pattern, a score is calculated based on symmetry, R² of trendlines, and volume.
def find_pivots(highs, lows, window=5):
pivot_highs = []
pivot_lows = []
for i in range(window, len(highs) - window):
if highs[i] == max(highs[i-window:i+window+1]):
pivot_highs.append((i, highs[i]))
if lows[i] == min(lows[i-window:i+window+1]):
pivot_lows.append((i, lows[i]))
return pivot_highs, pivot_lows
The window size affects the scale of detected patterns. For daily timeframes we use window=5, for hourly — window=3.
H&S algorithm details
For head and shoulders, we need a sequence of 5 pivot points: left shoulder, left neckline, head, right neckline, right shoulder. Conditions:
- Head is higher than both shoulders (tolerance ±2%)
- Both shoulders are approximately the same height (difference < 5%)
- Neckline is relatively horizontal (slope < 15°)
- Right shoulder does not exceed the head
Why backtesting on historical data is important
Without backtesting, you can't assess the real reliability of a pattern. Our backtesting module checks each detected pattern against history: did the target trigger within the next N candles? This allows calibrating thresholds for a specific asset. The algorithm learns from historical data to improve accuracy. According to Wikipedia, the head and shoulders pattern is considered one of the most reliable reversal patterns — Head and shoulders pattern. Our system shows a win rate of 55–65% for H&S on the crypto market.
| Pattern |
Win Rate |
Avg Reward/Risk Ratio |
| H&S (confirmed) |
55–65% |
1:1.5 |
| Ascending Triangle |
60–70% |
1:1.8 |
| Symmetrical Triangle |
50–55% |
1:1.2 |
| Falling Wedge |
60–68% |
1:2.0 |
Configuration parameters for different timeframes:
| Timeframe |
Pivot Window |
Minimum pattern life (candles) |
| 1H |
3 |
6 |
| 4H |
5 |
4 |
| 1D |
7 |
3 |
How to deploy the system: step by step
- Provide us with your requirements: assets, timeframes, pattern types.
- We develop the detector in Python using numpy and scipy.
- Integrate with your backend via REST API or WebSocket.
- Conduct backtesting on historical data over the past period.
- Deploy on a server and configure alerts.
Our algorithm detects patterns three times faster than standard Pine Script indicators. Traders save up to 25 hours per week of manual analysis, significantly reducing costs. Automation pays for itself within a few months. Project cost is determined individually after an analysis of complexity.
What's included in the work
- Requirements analysis and algorithm selection.
- Detector development tailored to your stack (Python, CCXT).
- Integration with exchange APIs (Binance, Bybit, Kraken).
- Backtesting on historical data with a report.
- Server deployment, documentation, and training.
System architecture
Backend: Python (pandas, numpy, scipy), OHLCV data processing via CCXT. Scanning is scheduled (cron) on each candle close.
Database: PostgreSQL for storing detected patterns with parameters and status.
Frontend: React + TradingView Lightweight Charts. Patterns are rendered as SVG overlays with labels.
Alerts: Telegram, Discord, or webhook on confirmed pattern or level breakout.
Scaling
For scanning 500+ instruments, parallel processing is used via Celery. Results are cached in Redis. A full scan of 500 instruments on the 4H timeframe takes 15–30 seconds on a standard server. Classic pattern definitions: Triangle pattern.
Our team has ten years of experience in blockchain development and over 500 implemented data analysis projects. We guarantee detection accuracy of at least 85% on test samples. Get a consultation on system integration — contact us to evaluate your project.
Why exchange development requires deep domain expertise
We develop exchanges — not 'chart sites,' but matching engines that process thousands of orders per second without delay, route liquidity between pools, and guarantee that no user gains access to others' funds. Teams that start with the UI and postpone the engine 'for later' end up rewriting everything in six months in 90% of cases.
Order Book vs AMM: where most projects break
Centralized exchanges (CEX) are built around an order book + matching engine. Decentralized exchanges (DEX) either also use an order book (dYdX on StarkEx, Serum/OpenBook on Solana) or an AMM with concentrated liquidity (Uniswap v3/v4, Curve, Balancer). A classic mistake when developing a CEX is implementing the matching engine on top of a relational database with transactions for each match. PostgreSQL handles ~500 RPS without special effort, but at peak loads of 5,000–10,000 orders per second, it turns into a deadlock nightmare. The correct architecture: in-memory order book (Redis Sorted Sets or custom C++/Rust structure), asynchronous writing of matches to PostgreSQL via a queue (Kafka/RabbitMQ), and a separate settlement service that finally updates balances.
For DEX, the most painful problem is sandwich attacks and MEV. A pool with a plain xy=k AMM without slippage protection becomes a target for MEV bots within hours of launch. Uniswap v2 lost hundreds of millions of dollars in user liquidity. Solutions: integration with Flashbots Protect, a commit-reveal scheme for orders, or switching to TWAMM (Time-Weighted AMM) for large trades.
Concentrated liquidity and impermanent loss
Uniswap v3 introduced concentrated liquidity – LPs choose a price range in which to provide liquidity. Capital efficiency increased 4,000x compared to v2 for stable pairs. But implementing this mechanism correctly is non-trivial. The Uniswap v3 liquidity contract uses tick-based accounting: the price space is divided into discrete ticks (tick = log₁.0001(price)), each tick stores accumulated fee growth and liquidity delta. When creating a position, the lower and upper ticks are computed, and the contract recalculates all active positions at each swap. Storage layout is critical here – incorrect variable packing in slots easily adds 40–60% to swap gas cost.
We implemented a Uniswap v3 fork for a client on Polygon with a custom fee tier system. The initial version consumed 180k gas for a swap across 2 ticks. After slot packing of variables in Tick.Info and inlining several internal calls, it dropped to 112k gas. This reduced gas costs by 38% and saved the client substantial costs on fees monthly. The techniques applied are described in the Uniswap v3 Whitepaper and confirmed by our audit experience.
How a matching engine delivers performance
A production-ready matching engine is built according to the following scheme:
-
Order ingestion layer – WebSocket gateway (Go or Rust), accepts orders, validates signature, checks balance via Redis, queues them. Latency at this level must be <1ms.
-
Matching core – single-threaded event loop (eliminates race conditions without mutexes). In memory, we hold two Sorted Sets for each trading instrument: bids and asks. FIFO matching for limit orders, immediate-or-cancel for market orders. Throughput with a proper Rust implementation – 500k–1M matches per second on a single core.
-
Settlement service – reads matches from Kafka, atomically updates balances in PostgreSQL (
UPDATE accounts SET balance = balance - $1 WHERE id = $2 AND balance >= $1). Optimistic locking via row versioning.
-
Withdrawal pipeline – separate service with cold/hot wallet architecture. The hot wallet holds 5–10% of total deposits, the rest is cold storage with multi-sig (Gnosis Safe or custom HSM). Automatic withdrawals only from hot wallet, large amounts require manual authorization.
| Component |
Technology |
Latency / Throughput |
| Order gateway |
Go + WebSocket |
<1ms p99 |
| Matching engine |
Rust (in-memory) |
500k+ orders/sec |
| Balance store |
Redis (write-through) |
<0.5ms |
| Settlement DB |
PostgreSQL 14+ |
~50k TPS with partitioning |
| Event streaming |
Apache Kafka |
1M+ events/sec |
| Blockchain node |
Geth / Solana validator |
depends on chain |
How our exchange development process ensures reliability
Smart contracts and gas optimization
For EVM-based DEX (Ethereum, Arbitrum, Optimism, Polygon), the entire critical path lives in Solidity. Main contracts: Pool, Factory, Router, PositionManager (for v3-like), and Quoter for off-chain calculations. Typical mistakes we see in audits:
Reentrancy via callback. Uniswap v3 uses flash swap with a callback (uniswapV3SwapCallback). If your router lacks a nonReentrant guard and you don't check msg.sender == pool, the contract gets drained via a nested call. This is not hypothetical – several v3 forks lost funds this way.
Oracle manipulation in AMM. If your contract uses the spot price from the pool for collateral calculation, it is front-runnable. Correct: TWAP over 30+ minutes (Uniswap v3 OracleLib) or an external oracle (Chainlink).
Unbounded loops in liquidity range. If a swap crosses many ticks in a row (price impact 80%+), gas may exceed the block limit. Need MAX_TICKS_CROSSED with partial fill and returning the remainder.
For Solana DEX (Anchor framework, Rust), the architecture is fundamentally different: account-based model, Program Derived Addresses (PDA) instead of storage, Cross-Program Invocations instead of internal calls. Solana's throughput (~3,000–4,000 TPS vs 15–30 on Ethereum mainnet) allows building on-chain order books – exactly what Phoenix DEX does.
Liquidity bootstrapping and aggregator integration
Launching a pool is not enough – you need to ensure liquidity at launch. Practical mechanisms:
-
Liquidity Bootstrapping Pool (LBP) – initial price is high, asset weights dynamically shift, creating selling pressure and even token distribution. Implemented in Balancer v2.
-
Initial Liquidity Offering via Uniswap v3 – adding liquidity in a narrow range around the initial price, then gradually expanding as volume grows. Requires active liquidity management or integration with Arrakis/Gamma.
-
Integration with 1inch, Paraswap, Li.Fi – aggregators bring traffic but require standard compliance: the pool must have correct
getAmountsOut, support ERC-20 approval/permit, and not have custom transfer hooks that break the aggregator's routing.
Development process and deliverables
Analytics and design begin with choosing the architectural model: CEX with custodial storage, non-custodial DEX, or hybrid (off-chain order book + on-chain settlement, like dYdX v3). This decision determines everything – regulatory load, tech stack, team.
Development proceeds in layers: first smart contracts with full Foundry coverage (fuzzing, invariant testing), then backend services, then integration layer, and finally frontend. Testing includes fork testing on mainnet via Foundry – we reproduce real liquidity conditions, not synthetic ones.
Audit is mandatory before mainnet deployment. For DEX contracts, minimally one firm with manual review (Trail of Bits, Spearbit, Code4rena contest). For CEX custody, audit of key storage processes. We guarantee all contracts undergo formal verification and fuzzing testing (Echidna, Foundry invariant).
Estimated timelines
| Exchange type |
Timeframe |
| DEX (AMM, xy=k) |
3 to 5 months |
| DEX with concentrated liquidity (v3-like) |
6 to 10 months |
| CEX (matching engine + custody + trading UI) |
8 to 14 months |
| Integration with existing protocol |
4 to 8 weeks |
Cost is calculated individually after a technical briefing: chain selection, throughput requirements, custodial model. Our certified engineers with 10+ years of experience will help you choose the optimal architecture and avoid common pitfalls. Contact our team for a detailed proposal.
Pitfalls to avoid at launch
- Forgetting the price oracle in AMM. Spot price can be manipulated with a flash loan in one transaction. If your lending protocol uses the spot price from its own pool, that's a bug.
- Hot wallet without limits. A CEX without daily limits on automatic withdrawals is an invitation for attackers. Compromising one key should lose at most 10% of total funds.
- Absence of circuit breaker. A 40% price drop in 5 minutes should halt automatic liquidations or withdrawals until manual review. Without this, a cascading liquidation spiral destroys all TVL.
- Incorrect decimal handling. USDC uses 6 decimals, WBTC – 8, most tokens – 18. Mixing without normalization leads to either precision loss or overflow. Solidity has no float; we work with fixed-point using FullMath (mulDiv with overflow protection).
Want to avoid these problems? Get a consultation — we will select the architecture for your project and provide exact timelines. Order exchange development with quality guarantee and ongoing support.
