Algorithmic trading — using predefined rules (algorithms) to buy and sell assets. Come to think of it, when a caveman picked up a stick and rolled a huge boulder off the path — that’s also an algorithm: “If rock blocks path в†’ pick up stick в†’ roll rock.”

Why this matters:

Algorithms free you from emotions, save time, and allow 24/7 trading. In crypto, this is especially important: the market never sleeps, but humans do.

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What is an Algorithm in Simple Terms

Algorithm — step-by-step instructions to achieve a goal.

Real-life examples:

1. Borscht recipe:

   • If you have beets в†’ grate them
   • If no meat в†’ add beans
   • Cook for 2 hours в†’ serve with sour cream

2. Getting ready for work:

   • If alarm rings в†’ get up
   • If raining в†’ take umbrella
   • If traffic jam в†’ turn on navigator

Trading algorithm:

• If price crosses moving average from below в†’ buy
• If price drops 5% from high в†’ sell
• If time is 18:00 в†’ close all positions

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History of Trading Algorithms

Pre-computer Era: Paper Rules

1920s: Jesse Livermore (legendary trader) uses “trading rules.”

His algorithm:

• If stock rises 3 days in a row в†’ buy
• If drops 10% from peak в†’ sell
• If news is bad в†’ reduce position

Result: One of the largest fortunes of the time ($100+ million), several bankruptcies due to violating his own rules among other factors.

Lesson: Algorithm works only if you follow it.

1950-1970: First Computers

1950s: Computers appear on Wall Street.

First algorithms:

• Calculating moving averages
• Simple conditions: “If MA(50) > MA(200) в†’ buy”

Problem: Room-sized computers, expensive, slow.

1971: NASDAQ — first electronic quotation system.

• Quotes on screens (not phone calls)
• But trading still by phone (market makers in offices)
• Fully electronic trading: late 1980s (SOES after 1987 crash)

Important: NYSE still used “open outcry” trading. NASDAQ was an alternative, but not a fully automated exchange in the modern sense.

1980-1990: Quantitative Analysis

1980s: Quant funds appear.

Renaissance Technologies (Jim Simons):

• Mathematicians, physicists, astronomers instead of traders
• Algorithms based on statistics, patterns
• Returns: 66% annually (1988-2018)

Lesson: Math beats intuition.

1990-2000: High-Frequency Trading (HFT)

1990s: Internet, fast computers.

HFT (High-Frequency Trading):

• Thousands of trades per second
• Profit from microscopic price differences
• Requires: direct connection to exchange, servers near exchange

HFT peak: 2000-2010.

• 60-70% of trading volume in US
• Scandals: Flash Crash 2010 (1000-point drop in minutes)

2010-2020: Cryptocurrencies and Retail Bots

2010: Bitcoin starts trading on exchanges.

Crypto market features:

• 24/7 (no weekends)
• High volatility (В±10% per day — normal)
• Many exchanges (arbitrage opportunities)

2015-2020: Bots for retail traders.

• 3Commas, Cryptohopper, HaasOnline
• Ready strategies: DCA, Grid, Trailing Stop
• Accessible to everyone ($20-100/month)

2020-2026: AI and Machine Learning

2020s: Neural networks in trading.

What AI can do:

• Analyze news (NLP — natural language processing)
• Recognize patterns on charts (CNN — convolutional networks)
• Optimize strategy parameters (RL — reinforcement learning)

Problem:

• AI requires data (years of history)
• AI doesn’t understand “black swans” (unpredictable events)
• AI can overfit (works only on historical data)

Important: Using ready-made “AI bots” from marketplaces is often a marketing gimmick. Training models requires data scientist qualifications, not just parameter tweaking.

Conclusion: AI is a tool, not a magic wand.

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Types of Trading Algorithms

1. Trend Strategies

Idea: “The trend is your friend.”

Algorithm:

• If price above MA(50) and MA(200) в†’ long
• If price below MA(50) and MA(200) в†’ short
• If MAs cross в†’ exit

Example:

• BTC rises from $60,000 to $100,000 (6 months)
• Algorithm bought at $65,000, sold at $95,000
• Profit: +46%

Pros:

• Catches big moves
• Simple logic

Cons:

• Loses in sideways (frequent false signals)
• Lag (enters late)

2. Counter-Trend Strategies

Idea: “What goes up must come down.”

Algorithm:

• If RSI > 70 (overbought) в†’ short
• If RSI < 30 (oversold) в†’ long
• If RSI normal в†’ wait

Example:

• BTC dropped from $100,000 to $80,000 in a day
• RSI = 25 (strong oversold)
• Algorithm bought at $80,000, sold at $90,000
• Profit: +12.5%

Pros:

• Works in sideways
• Fast trades

Cons:

• Dangerous in strong trend (can catch a falling knife)
• Requires precise entry points

3. Arbitrage (for Advanced)

Idea: “Buy cheap on one exchange, sell expensive on another.”

Algorithm:

• If BTC on Bybit = $95,000, and on Binance = $95,300
• Buy on Bybit, sell on Binance
• Profit: $300 (minus fees)

Reality for retail trader:

• Price difference between major exchanges: 0.1-0.3% (rarely more)
• Opportunities disappear in milliseconds (HFT competition)
• For capital < $10,000, arbitrage is practically inaccessible
• More realistic: futures arbitrage (basis trading), pair arbitrage

Pros:

• Low risk (market doesn’t matter)
• Predictable profit (if you made it)

Cons:

• Need capital on two exchanges
• Fees eat profit
• Competition with HFT (need low latency)
• Not recommended for capital < $10,000

4. Market Making

Idea: “Profit from the spread (difference between buy and sell).”

Algorithm:

• Place limit buy at $94,900
• Place limit sell at $95,100
• Spread: $200
• If both sides executed в†’ profit $200

Example:

• Spread: 0.2-0.5%
• Volume: $1,000,000/day
• Profit: $2,000-5,000/day (minus risks)

Rebates:

Exchanges pay makers for providing liquidity. Maker fee is often negative (e.g., -0.005% to -0.025%), meaning you get paid for placing limit orders.

Pros:

• Profit in any market
• Exchanges pay rebates

Cons:

• Risk of one-sided position (price moved, order stayed)
• Requires constant monitoring
• Need large capital for noticeable profit

5. DCA (Dollar Cost Averaging)

Idea: “Buy regularly, regardless of price.”

Algorithm:

• Every Monday buy $100
• Doesn’t matter if price is $50,000 or $100,000
• Averaging entry price

Example:

• Week 1: $100 / $50,000 = 0.002 BTC
• Week 2: $100 / $60,000 = 0.00167 BTC
• Week 3: $100 / $55,000 = 0.00182 BTC
• Total: $300 / 0.00549 BTC = $54,645 average price

Pros:

• вњ… No emotions
• вњ… Volatility averaging
• вњ… Suitable for long-term investments

Cons:

• вќЊ Doesn’t maximize profit (could have bought at the bottom)
• вќЊ Requires discipline

6. Grid Trading

Idea: “Place a grid of orders within a range.”

Algorithm:

• Range: $90,000-100,000
• Grid step: $1,000
• Place 10 buy orders ($90k, $91k, … $99k)
• Place 10 sell orders ($91k, $92k, … $100k)
• If price oscillates в†’ profit on each cycle

Example:

• Price: $95,000 в†’ $96,000 в†’ $95,000 в†’ $96,000
• Bought at $95k, sold at $96k в†’ +$1,000
• Repeated 10 times в†’ +$10,000

Pros:

• вњ… Works in sideways (70% of the time)
• вњ… Automatic, 24/7

Cons:

• вќЊ Dangerous in trend (price exited range)
• вќЊ Requires large capital (many orders)
• вљ пёЏ Important: If price exits the range, the bot may end up with a full bag of losing assets (bought everything, didn’t sell).

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Algorithms on Traditional Markets vs Crypto

Parameter	Traditional Markets	Crypto Markets
Trading hours	9:30-16:00 (exchange hours)	24/7
Volatility	1-3% per day	2-10% per day (depends on asset and market phase)
Fees	0.01-0.1%	0.02-0.1%
Leverage	Up to 100x (futures)	Up to 125x (crypto exchanges)
Regulation	SEC, CFTC, strict rules	Weak (depends on jurisdiction)
HFT	60-70% of volume	20-50% of volume (estimates vary, exact data is proprietary)
Accessibility	Qualified investors	Anyone with internet

Conclusion: Crypto is more accessible, more volatile, but riskier. HFT share in crypto is estimated at 20-50% for centralized exchanges (CEX), but exact data is proprietary.

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How to Create Your First Algorithm

Step 1: Choose a Strategy

Questions:

• What’s my capital? ($100, $1,000, $10,000)
• How much time do I have? (full-time, 1 hour/day, once a week)
• What risk is acceptable? (1%, 5%, 10% of capital)

Recommendations:

• Capital < $1,000: DCA, Grid (low fees)
• Capital $1,000-10,000: Trend, counter-trend
• Capital > $10,000: Arbitrage, market making

Step 2: Define Rules

Template:

IF [entry condition] в†’ THEN [action]
IF [exit condition] в†’ THEN [action]
IF [risk condition] в†’ THEN [action]

Example (trend):

IF price > MA(50) AND MA(50) > MA(200) в†’ BUY
IF price < MA(50) в†’ SELL
IF loss > 5% в†’ STOP-LOSS

Step 3: Backtest on Historical Data

Backtest — testing a strategy on historical data to evaluate performance.

Tools:

MT4/MT5 Terminals:

• MQL4/MQL5 language
• Built-in strategy tester
• Expert Advisors (automated strategies)
• Pros: Powerful, many ready solutions
• Cons: Forex/CFD only, paid licenses

TradingView:

• Pine Script language
• Simple syntax (5-10 lines for basic strategy)
• Built-in backtester
• Pros: Free, many cryptocurrencies, cloud-based
• Cons: Limited logic vs Python

Python Libraries:

• Backtrader, Freqtrade, CCXT
• Pros: Full freedom, free
• Cons: Need coding, setup

Ready Platforms:

• Veles: Crypto algo trading, backtests, cloud bots — try Veles (affiliate link)
• Gainium: Grid, DCA, market making (free up to 2 bots)
• 3Commas: DCA, Grid, Trailing
• Antbot: Copy trading, signals
• Ginarea: Visual strategy builder focused on futures

What to check:

• Returns (% per period)
• Drawdown (max peak-to-trough drop)
• Number of trades (statistical significance)
• Win rate (% of profitable trades)

Step 4: Launch with Real Money

Rules:

1. Start small (10% of capital)
2. Monitor first trades
3. Keep a journal (why entered, why exited)
4. Don’t change algorithm after 1-2 losing trades

Step 5: Analyze and Optimize

Metrics:

• Sharpe Ratio: Return / risk (good > 1)
• Max Drawdown: Maximum drop (good < 20%)
• Profit Factor: Profit / Loss (good > 1.5)
• Win Rate: % of profitable trades (good > 50%)

Important: Don’t over-optimize!

• If you change parameters to fit history в†’ algorithm won’t work on new data
• Better a simple algorithm than an overfitted one

Keeping a journal:

Alongside the algorithm, it’s recommended to keep a trading journal — record why you entered a trade, why you exited, what emotions you felt. This helps identify behavioral patterns and improve discipline.

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Risks of Algorithmic Trading

1. Technical Failure

Problem: Exchange down, internet lost, bot froze.

Example:

• Bot was supposed to sell at 5% loss
• Exchange didn’t respond for 10 minutes
• Loss: 20% instead of 5%

How to protect:

• VPS (virtual private server) near exchange
• Backup internet (4G + wired)
• Stop-loss on exchange (not just in bot)

2. “Black Swan”

Problem: Unpredictable event (exchange collapse, war, Elon Musk tweet).

Example:

• FTX collapse (November 2022)
• BTC dropped from $21,000 to $16,000 in a day
• Algorithms weren’t ready

How to protect:

• Diversification (not everything on one exchange)
• Position limits (no more 10% per asset)
• Manual control during crisis

3. Overfitting

Problem: Algorithm works perfectly on history, but not on real data.

Example:

• Backtest: +500% per year
• Reality: -50% per month

How to protect:

• Test on different periods (bull, bear, sideways)
• Don’t change parameters after every losing trade
• Use out-of-sample data (didn’t participate in optimization)

4. Competition

Problem: Other algorithms are faster, smarter, with more capital.

Example:

• Arbitrage opportunity disappears in 100ms
• HFT fund makes it, retail trader doesn’t

How to protect:

• Choose niches where HFT is ineffective (long-term strategies)
• Use unique data (alternative sources)
• Focus on risk management, not speed

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Tools for Algo Trading

For Beginners (No Code)

• Veles: Cloud bots, backtests, simple interface — try Veles (affiliate link)
• Gainium: Grid, DCA, market making (free up to 2 bots)
• 3Commas: DCA, Grid, Trailing
• Cryptohopper: Ready strategies, marketplace
• Antbot: Signals, copy trading
• TradingView: Signals, alerts, Pine Script (gold standard for visual backtesting)

Algo Trading Platforms Comparison

Platform	Type	Difficulty	Cost	Crypto	Forex/CFD	Backtests
Veles	Cloud	⭐	Free	✅	❌	✅
Gainium	Cloud	⭐	Freemium	✅	❌	✅
Ginarea	Cloud	⭐⭐	Freemium	✅	✅	⚠️ Partial
3Commas	Cloud	⭐⭐	$20-100/mo	✅	❌	⚠️
TradingView	Web	⭐⭐	$0-15/mo	✅	✅	✅
MT4/MT5	Desktop	⭐⭐⭐	Free	❌	✅	✅
Python + CCXT	Code	⭐⭐⭐⭐	Free	✅	❌	✅
QuantConnect	Cloud	⭐⭐⭐⭐⭐	$0-1000/mo	✅	✅	✅

Difficulty: ⭐ Very Easy | ⭐⭐⭐⭐⭐ Requires Programming

For Advanced (Code)

• Python + CCXT: Library for exchanges (free)
• Backtrader: Backtesting (free)
• Freqtrade: Open-source crypto bot (free)

For Pros

• QuantConnect: Platform for quants ($0-1,000/month)
• Interactive Brokers API: Traditional markets (commissions)
• Own infrastructure: Servers, direct connection (expensive)

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Summary

Algorithmic trading — using rules for automated trading. From “caveman’s stick” to AI bots.

Main rules:

1. Simple algorithm is better than complex
2. Test on history, but don’t over-optimize
3. Risk management is more important than returns
4. Algorithm doesn’t replace thinking, it enhances it

Who algo trading is for:

• вњ… Traders who want to automate routine
• вњ… Investors for DCA and rebalancing
• вњ… Developers who love code + finance

Who it’s NOT for:

• вќЊ Expecting a “money button” (algorithm is a tool, not magic)
• вќЊ Not ready to learn (code, statistics, risks)
• вќЊ Want 100% guarantees (they don’t exist even with algorithm)

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FAQ

Do I need to know how to code?

No. There are ready platforms (3Commas, Cryptohopper). But coding gives more flexibility and control.

How much capital do I need?

From $100 for DCA/Grid. From $1,000 for trend strategies. From $10,000 for arbitrage.

Can I make a living from algo trading?

Theoretically yes, but it requires capital of $100,000+ and returns of 10-20% annually. For most, it’s a supplement to main income.

Important: For retail traders with small capital ($1k-10k), trying to “make a living from it” immediately may lead to excessive risk and loss of deposit. However, you can start with a small amount, gradually grow your deposit, and build capital through profit reinvestment.

What risk is acceptable?

No more than 1-2% of capital per trade. Maximum drawdown — 20% (after that, review strategy).

How to choose an exchange for algo trading?

Criteria: low fees (0.02-0.1%), high liquidity, API for bots, reliability (Bybit, Binance, OKX).

What is a backtest?

Testing a strategy on historical data. Shows how the algorithm would have performed in the past. Doesn’t guarantee future results.

Can AI replace a trader?

AI is a tool, like a calculator. It doesn’t replace thinking, but speeds up analysis. Final decision is still up to the human.