Trading Strategies

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name: trading description: Comprehensive trading knowledge base covering fundamentals, technicals, strategies, backtesting, and risk management. Use when building trading apps or evaluating strategies.

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Trading Skill — Complete Reference

Purpose

Comprehensive trading knowledge base covering fundamentals, technicals, strategies, backtesting, and risk management. Use when building trading apps, evaluating strategies, or making trading decisions.

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1. TRADING STYLES

Scalping

Hold: seconds to minutes

Goal: profit from tiny price movements

Pros: many opportunities, reduced exposure to big moves

Cons: high transaction costs, stressful, tiny profit per trade

Best for: highly liquid markets with tight spreads

Day Trading

Hold: minutes to hours (close all by market close)

Goal: profit from intraday price movements

Pros: no overnight risk, high profit potential per trade

Cons: high risk, emotional pressure, costs add up

Best for: volatile stocks with clear intraday patterns

Swing Trading

Hold: days to months

Goal: catch short-to-intermediate moves

Pros: lower costs, more analysis time, less stressful

Cons: overnight risk, may miss long-term moves

Best for: trending markets with pullbacks

Position Trading

Hold: months to years

Goal: profit from major long-term trends

Pros: lowest costs, highest profit potential, most flexibility

Cons: capital tied up, exposed to macro events

Best for: fundamentally strong companies in sector uptrends

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2. FUNDAMENTAL ANALYSIS

Key Metrics

P/E Ratio (Price-to-Earnings)

Formula: Share Price / Earnings Per Share

S&P 500 average: ~30 (as of late 2025)

Low (<15): potentially undervalued or troubled

High (>30): potentially overvalued or high growth expected

Compare WITHIN same industry only

Forward P/E uses projected earnings; Trailing P/E uses last 12 months

P/B Ratio (Price-to-Book)

Formula: Share Price / Book Value Per Share

Book Value = total assets - intangible assets - liabilities

P/B < 1 = trading below asset value (potential bargain)

Most useful for capital-heavy industries (banks, manufacturing)

Debt-to-Equity Ratio

Formula: Total Liabilities / Shareholders' Equity

High = heavy debt reliance (risky in downturns)

Compare within industry — some sectors carry more debt naturally

Revenue Growth Rate

Year-over-year revenue increase

Consistent > spiky

Accelerating growth rate = strongest signal

Free Cash Flow (FCF)

Cash generated after capital expenditures

Positive FCF = real cash generation, not accounting tricks

FCF Yield = FCF / Market Cap (higher = better value)

EPS Growth (Earnings Per Share)

Consistent EPS growth over 3-5 years = strong signal

Check quality: operations vs one-time events

Return on Equity (ROE)

Formula: Net Income / Shareholders' Equity

ROE > 15% = generally good management efficiency

Dividend Yield & Payout Ratio

Yield = Annual Dividend / Share Price

Payout = Dividends / Net Income

Payout > 80% may be unsustainable

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3. TECHNICAL INDICATORS

RSI (Relative Strength Index)

Formula: RSI = 100 - (100 / (1 + (Avg Gain / Avg Loss))) over 14 periods

Scale: 0-100

>70 = Overbought (potential sell)

<30 = Oversold (potential buy)

Strengths: Simple, effective in ranging markets

Weaknesses: Stays >70 for extended periods in strong uptrends — don't auto-sell

RSI Divergence: Price makes new high but RSI makes lower high = bearish (momentum weakening)

Swing Rejection: RSI crosses 30 upward → dips but stays above 30 → breaks prior high = bullish entry

Best in: Ranging/sideways markets. Combine with ADX to filter.

MACD (Moving Average Convergence Divergence)

MACD Line = 12-period EMA - 26-period EMA

Signal Line = 9-period EMA of MACD Line

Histogram = MACD Line - Signal Line

Buy: MACD crosses ABOVE signal line

Sell: MACD crosses BELOW signal line

Divergence: MACD rising while price falling = potential reversal

Strengths: Good trend-following indicator

Weaknesses: Lagging, many false positives in sideways markets

Best combined with ADX — only trust MACD signals when ADX > 25 (confirming trend)

Bollinger Bands

Middle Band = 20-day SMA

Upper Band = SMA + 2 standard deviations

Lower Band = SMA - 2 standard deviations

95% of price action stays within bands

Squeeze: Bands narrow → low volatility → breakout imminent (direction unknown)

Bollinger Bounce: Price off lower band toward middle = buy; off upper toward middle = sell

Breakout: Price outside bands with volume = trend continuation

Strengths: Visual, adapts to volatility

Weaknesses: Secondary indicator — always confirm with RSI/MACD

Created by John Bollinger in the 1980s

Moving Averages

SMA (Simple): Equal weight all periods

EMA (Exponential): More weight on recent prices (faster reaction)

Key periods: 5, 9, 20, 50, 100, 200 day

Golden Cross: 50-day crosses ABOVE 200-day = strong bullish

Death Cross: 50-day crosses BELOW 200-day = strong bearish

MAs act as dynamic support/resistance levels

Use longer MAs for volatile stocks to avoid false signals

Volume

Confirms price movements

Price up + high volume = strong bullish

Price up + low volume = weak rally, likely to reverse

Price down + high volume = strong selling pressure

Price down + low volume = lack of selling conviction

60-80% of daily volume is algorithmic

Ichimoku Cloud (Ichimoku Kinko Hyo)

5 Components:

- Tenkan-sen (Conversion Line): (9-period high + 9-period low) / 2 - Kijun-sen (Base Line): (26-period high + 26-period low) / 2 - Senkou Span A: (Tenkan + Kijun) / 2, plotted 26 periods ahead - Senkou Span B: (52-period high + 52-period low) / 2, plotted 26 periods ahead - Chikou Span: Current close plotted 26 periods back

The Cloud (Kumo): Area between Span A and Span B = support/resistance zone

Buy: Price above cloud, Tenkan crosses above Kijun

Sell: Price below cloud, Tenkan crosses below Kijun

Strengths: All-in-one indicator (trend, momentum, support/resistance)

Weaknesses: Complex visually, lagging in fast markets

Best in: Trending markets. Avoid in ranging/sideways.

Michael Automates created an AI-improved Ichimoku strategy that performs well

Super Trend

Formula: Based on ATR (Average True Range) and a multiplier

Upper Band = (High + Low) / 2 + (Multiplier × ATR)

Lower Band = (High + Low) / 2 - (Multiplier × ATR)

Buy: Price crosses above Super Trend line

Sell: Price crosses below Super Trend line

Strengths: Simple, good trend-following, clear signals

Weaknesses: Late entries, bad in ranging markets

AI iteration took Super Trend from 44% to 3,605% P&L in Michael Automates' testing

VWAP (Volume-Weighted Average Price)

Formula: Sum(Price × Volume) / Total Volume (intraday only)

Above VWAP = bullish intraday

Below VWAP = bearish intraday

Institutional benchmark — they buy below VWAP, sell above

Resets daily — intraday tool only

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4. SIGNAL COMBINATIONS

Strong Buy Signal (High Confidence)

All of these together:

RSI < 30 (oversold) AND recovering

MACD crossing above signal line

Price bouncing off lower Bollinger Band

Volume increasing on the bounce

Price above 200-day MA (long-term uptrend intact)

Fundamentals solid (P/E reasonable, FCF positive, revenue growing)

Moderate Buy Signal

RSI between 40-50 in an uptrend (pullback buy)

Price touching 50-day MA support

MACD histogram turning positive

Volume above average

Trend-Following Entry

Golden Cross (50-day crosses above 200-day)

Price breaks above resistance with high volume

ADX > 25 confirming trend strength

Buy on first pullback after breakout

Strong Sell Signal

RSI > 70 in a ranging market

Price hitting upper Bollinger Band with declining volume

MACD crossing below signal line

Price reaching prior resistance level

RULE: Never rely on a single indicator. Always combine 2-3 minimum.

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5. POSITION SIZING

The 1% Rule

Never risk more than 1% of total account on a single trade.

Account: $1,000 → max risk per trade: $10

If stop-loss is $2 below entry → buy 5 shares max

Keeps you alive through losing streaks

Kelly Criterion

f = (bp - q) / b

b = win/loss ratio, p = win probability, q = loss probability

Gives optimal position size based on historical win rate

Use half-Kelly for safety (most professionals do)

Example

Win rate: 55%, average win: $200, average loss: $100

Kelly: f = (2 × 0.55 - 0.45) / 2 = 0.325 (32.5% of account)

Half-Kelly: 16.25% — still aggressive, many pros use quarter-Kelly

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6. EXIT STRATEGIES

Stop-Loss Types

Fixed percentage: Sell if price drops X% (typically 5-8%)

ATR-based: Stop at entry minus 2× Average True Range (adjusts for volatility)

Support-based: Stop below key support level or moving average

Trailing stop: Moves up with price, locks in profits (e.g., trail by 5%)

Take-Profit Methods

Fixed target: Pre-set price target (e.g., 2:1 or 3:1 risk/reward)

Technical target: Previous resistance level, Fibonacci extension

Trailing: Let winners run with trailing stop

The 3 Hardest Sells

1. Cutting losses — "it'll come back" kills accounts. Honor your stops. 2. Taking profits too early — use trailing stops to let winners run 3. Holding through earnings — volatility spikes. Reduce position or hedge.

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7. STRATEGY ARCHETYPES

Mean Reversion

Theory: Prices revert to their historical average over time

Entry: Buy when RSI < 30 or price 2+ standard deviations below mean

Exit: Sell when RSI > 70 or price returns to mean

Tools: Z-scores, Bollinger Bands, RSI

Best in: Range-bound/sideways markets

Weakness: Fails badly in strong trending markets (price keeps going)

Key stat: Z-score above 1.5 or below -1.5 signals opportunity

Momentum

Theory: Stocks that are going up tend to keep going up (and vice versa)

Entry: Buy stocks with strongest 3-6 month performance

Exit: Sell when momentum weakens (MACD crossover, RSI divergence)

Philosophy: "Buy high, sell higher" — opposite of value investing

Best in: Trending markets with clear direction

Weakness: Sudden reversals can be devastating

Key tools: Trend lines, MACD, RSI, relative strength vs index

Moving Average Crossover

Entry: Buy on Golden Cross (50-day crosses above 200-day)

Exit: Sell on Death Cross (50-day crosses below 200-day)

Pros: Simple, catches major trends

Cons: Very lagging — you'll miss the first 10-20% of a move and give back 10-20% at the end

Variant: Use shorter MAs (9/21) for faster signals but more noise

Bollinger Band Squeeze

Entry: When bands contract to minimum width, enter on the breakout direction with volume confirmation

Exit: When price reaches opposite band or bands start contracting again

Key: The squeeze only tells you a big move is coming — NOT the direction

Must combine with: Volume, MACD, or other directional indicator

Pairs Trading

Origin: Morgan Stanley, mid-1980s

How: Find two highly correlated stocks (0.80+ correlation). When they diverge, go long the underperformer, short the outperformer

Profit: When they converge back to historical correlation

Market-neutral: Hedged against broad market moves

Risk: Correlation can break permanently (one company's fundamentals change)

Example: Coca-Cola vs Pepsi, Visa vs Mastercard

VWAP Reversion (Intraday)

Entry: Buy below VWAP when overall trend is bullish

Exit: Sell above VWAP or at end of day

Best for: Day trading liquid stocks

Why it works: Institutional traders use VWAP as a benchmark

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8. BACKTESTING

The 4 Deadly Biases

1. Optimization Bias (Curve Fitting): Over-tuning to historical data. Fix: minimal parameters, out-of-sample testing 2. Look-Ahead Bias: Using future data accidentally. Fix: strict chronological processing 3. Survivorship Bias: Only testing stocks that exist today. Fix: survivorship-free datasets or recent data 4. Psychological Tolerance Bias: A 25% drawdown looks fine on a chart but feels devastating in real-time

Key Metrics

| Metric | Formula | Good | Great | |--------|---------|------|-------| | Sharpe Ratio | (Return - Risk-Free) / Std Dev | >1 | >2 | | Max Drawdown | Worst peak-to-trough | <20% | <10% | | Win Rate | Wins / Total Trades | >40% | >55% | | Profit Factor | Gross Profits / Gross Losses | >1.5 | >2 | | Risk/Reward | Avg Win / Avg Loss | >2:1 | >3:1 |

Expected Return per Trade: (Win% × Avg Win) + (Loss% × Avg Loss) — must be positive

Drawdown Math (Critical)

10% loss → need 11% gain to recover

20% loss → need 25% gain to recover

30% loss → need 43% gain to recover

50% loss → need 100% gain to recover

The math gets exponentially worse. Avoiding big losses > chasing big wins.

Process

1. Define strategy rules with zero ambiguity 2. Get clean OHLCV data (Open, High, Low, Close, Volume) 3. Build engine (Python: backtrader, vectorbt, quantstats) 4. Split: 70% training / 30% out-of-sample validation 5. Run Monte Carlo simulation (randomize trade order) for robustness 6. Paper trade winners for 3+ months 7. Go live small — start with minimum position sizes

Michael Automates Backtesting Workflow (Proven)

His backtesting engine ($99) does this — we can replicate for free: 1. Get historical OHLCV data (from CCXT/Binance/Alpaca — free) 2. AI creates Python version of your trading strategy 3. Run backtest locally → get metrics 4. Compare with TradingView numbers to verify accuracy 5. If numbers don't match: Export TradingView Excel report, do trade-by-trade comparison 6. AI can auto-fix discrepancies by modifying the backtest engine code 7. Critical settings: Commission = 0.1%, timezone = UTC, date range = not full history

Auto-download data:

CCXT pulls from Binance, Coinbase, Kraken automatically

Store in /data/cache/ folder by asset and timeframe

Format: BTC_USDT_1d.csv, ETH_USDT_4h.csv, etc.

Data Sources

Free: Yahoo Finance (yfinance), Alpha Vantage, Alpaca, Polygon.io free tier, CCXT (100+ crypto exchanges)

Paid: Norgate, Nasdaq Data Link (Quandl)

Warning: Yahoo Finance has survivorship bias

Best for crypto: CCXT + Binance (most history, most pairs)

Best for stocks: Alpaca (free, clean data, built-in paper trading)

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9. RISK MANAGEMENT

Core Rules

1% Rule: Never risk more than 1% of account per trade

Daily Loss Limit: Stop trading if down 3% in a day

Correlation Risk: Don't hold 5 tech stocks — one sector crash kills you

Position Limits: Max 5-10 open positions at once

Cash Reserve: Always keep 20-30% cash for opportunities

Circuit Breakers

Hit daily loss limit → done for the day

Hit weekly loss limit (5%) → reduce position sizes 50%

Hit monthly loss limit (10%) → pause, review all strategies

3 consecutive losses → take a break, re-evaluate

Fees & Slippage (Often Ignored)

Commission-free doesn't mean cost-free — there's still the spread

Slippage: the difference between expected and actual execution price

High-frequency strategies amplify these costs

Always include fees + slippage in backtests or results are meaningless

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10. COMMON MISTAKES

1. Trading without a plan → Write rules BEFORE trading 2. Ignoring position sizing → The 1% rule exists for a reason 3. Moving stop-losses → Set them and honor them 4. Averaging down → Adding to losers hoping they'll recover 5. Overtrading → More trades ≠ more profit (costs eat you alive) 6. Single indicator reliance → Always combine 2-3 indicators 7. Not accounting for fees → Especially in backtests 8. Skipping paper trading → Going live without 3+ months of validation 9. Revenge trading → Trying to win back losses with bigger bets 10. Ignoring the macro → Individual stocks don't exist in a vacuum

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11. TOOLS & APIS

Broker APIs

Alpaca — Commission-free, great API, paper trading built in (RECOMMENDED for us)

Interactive Brokers — Most comprehensive, supports everything

TD Ameritrade/Schwab — thinkorswim API

Data APIs

Alpha Vantage — Free tier (5 calls/min), stocks + crypto + forex

Polygon.io — Real-time and historical, free tier

Yahoo Finance — Free via yfinance (unreliable, survivorship bias)

Alpaca Market Data — Included with account

Python Stack

pandas — Data manipulation

numpy — Numerical computing

ta-lib / pandas-ta — Technical indicators

backtrader — Full backtesting framework

vectorbt — Fast vectorized backtesting

quantstats — Portfolio analytics

scikit-learn — ML pattern recognition

alpaca-trade-api — Broker integration

Architecture

[Data Feed] → [Indicator Engine] → [Signal Generator] → [Risk Manager] → [Order Executor]
     ↑                                                           |
     └──────────── [Backtesting Engine] ←────────────────────────┘
                          ↓
                   [Performance Analytics]

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12. CRYPTO-SPECIFIC STRATEGIES (From Video Research)

Super Trend Strategy

Built-in TradingView indicator

AI can iterate: base version (44% P&L) → optimized V4 (3,605% P&L)

Process: give AI base → "improve it" → backtest → repeat overnight

Test on multiple assets to avoid overfitting

Strategy Tournament (Evolution Method)

Create 10 different strategies with different parameters/logic

Run all 10 in parallel with small positions ($100 each)

After 1-2 weeks: cut losers, keep winners

Create variations of winners, repeat process

Natural selection for trading strategies

Market Regime Detection

Run separate strategies for trending vs ranging markets

AI detects current regime and switches strategies automatically

Trend-following for trending markets, mean reversion for ranging

Prediction Market Strategies (Polymarket)

15-Minute Windows: BTC up/down resolution every 15 minutes

Late Entry: Look at trend in last 3-4 minutes, enter in that direction

Arbitrage: When both sides cost <$1 total = guaranteed profit

Mention Markets: AI studies speech patterns to predict word usage

Sports Scanner: AI researches obscure low-volume markets for edges

Counter-Trend AI: Bet against AI consensus (contrarian)

Wallet Analysis: Copy successful wallets → AI reverse-engineers their strategy

KEY: Not dependent on bull/bear markets — works always

Multi-Agent Trading Architecture

Coordinator: Delegates tasks, manages priorities, daily briefing

Quant Scanner: Scans 30+ coins every 15 min with CCXT, confluence scoring

Researcher: Daily news/intel scanning, deep dives

Alert Agent: Formats and delivers signals

Security Agent: 24hr audit for malicious code, prompt injections, bugs

Each agent needs a detailed "soul" (personality + instructions)

Agents work in parallel, not sequentially

Crypto Exchange APIs

Hyperliquid — Decentralized perps, no KYC, good API

Blofin — Centralized, API with trading perms (disable withdrawals!)

CCXT Library — Open-source, supports 100+ exchanges, start read-only

Alpaca — Stocks (commission-free, paper trading built in)

AI Strategy Iteration Workflow (KEY — From Michael Automates)

This is the single most powerful workflow from all our research: 1. Pick a base strategy (e.g., Super Trend, RSI+MACD, Bollinger Squeeze) 2. AI backtests it → gets baseline metrics (P&L, drawdown, win rate, Sharpe) 3. Tell AI "improve this" → it modifies parameters, adds filters, changes logic 4. AI creates V2, V3, V4... each iteration potentially better 5. Compare AI numbers with TradingView to verify accuracy 6. LET IT RUN OVERNIGHT — wake up to results 7. Score across multiple assets (BTC, ETH, SOL, SPY, AAPL) to avoid overfitting 8. Only keep strategies that work on 3+ different assets 9. Repeat until you have top 3 strategies → paper trade those

Michael's results: Super Trend went from 44% P&L → 3,605% P&L through this process.

Critical anti-overfitting rules:

Test on multiple assets, not just one

Use walk-forward validation (train on years 1-3, test on years 4-5)

If a strategy only works on one asset, it's overfit — discard it

Commission must be set to 0.1% (not 0%)

TradingView timezone must be UTC for number matching

Strategy Tournament (From Alex Carter)

1. Create 10 different strategies (vary parameters, timeframes, indicators) 2. Run ALL 10 in parallel with small positions ($100 each) 3. Track for 1-2 weeks 4. Kill bottom 5 performers 5. Create mutations/variations of top 5 6. Run new tournament 7. Repeat — "evolution for trading strategies" 8. After 3-4 rounds, surviving strategies are battle-tested

Counter-Trend Against AI Crowd (From Coin Bureau)

Most AI trading bots use similar data and strategies

When AI crowd consensus is heavy one direction, bet the opposite

Works because: AI herding creates overcrowded trades that reverse

Requires: monitoring what popular AI strategies are doing

Best for: short-term contrarian plays in crypto and prediction markets

Divergence Trading (From Coin Bureau)

Use TBT Divergence indicator on 15-minute timeframe

Contrarian entry when price makes new high/low but indicator doesn't confirm

Specifically: price up but RSI/MACD diverging = weakening momentum

Enter against the trend on confirmed divergence

Best for: short-term crypto and Polymarket 15-min windows

Critical Crypto Trading Rules

NEVER enable withdrawal permissions on API keys

Whitelist your IP on centralized exchanges

Store keys in .env files, never hardcoded

Use testnet/paper trading first

Rotate API keys every few months

Run on dedicated machine (not personal computer)

Daily security audit via cron job

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13. POLYMARKET (PREDICTION MARKETS)

How It Works

Binary markets: buy Yes/No shares in real-world outcomes

Correct = $1.00 payout, wrong = $0.00

Price = probability (Yes at $0.70 = market thinks 70% likely)

Runs on Polygon (Ethereum L2), uses USDC.e

Python SDK: py-clob-client

Public data API (no auth): gamma-api.polymarket.com

Strategy Types

1. Arbitrage — Yes + No < $1.00 = free money (rare, need speed) 2. Late Entry — 15-min BTC windows, enter last 3-4 min when trend is clear 3. Mention Markets — AI analyzes speech patterns to predict word usage 4. Obscure Sports — Low-volume markets with inefficient pricing 5. Wallet Analysis — Reverse-engineer top performers' strategies 6. Market Making — Provide liquidity, profit from spread (advanced)

Why AI Has an Edge

Can process more data than any human (speeches, news, stats)

24/7 monitoring of all markets simultaneously

Niche markets have little competition = more mispricing

Uncorrelated to stock/crypto markets — works in any cycle

Market Making (Advanced)

Place both buy and sell orders, profit from the spread

Requires: understanding inventory risk, dynamic spread adjustment

Need algorithm to adjust quotes based on position, volatility, flow

Complex math but AI handles research and implementation

Start with wide spreads in low-volume markets, tighten as you learn

Risk: getting stuck holding losing positions if market moves fast

TBO Cloud Strategy (Coin Bureau — Proprietary)

Trending Breakout indicator on 4-hour timeframe

Advanced moving average strategy — details not public

Combined with TBT Divergence indicator for entry/exit

We can build similar: EMA cloud (9/21/55 EMAs) with breakout confirmation

Key concept: cloud acts as dynamic support/resistance, breakout above = long

Key Insight

Build scanner first (read-only, free). Paper trade. Then go live small.

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14. CRYPTO vs STOCK STRATEGIES — WHAT TRANSFERS

Strategies That Work in BOTH Markets

| Strategy | Stocks | Crypto | Notes | |----------|--------|--------|-------| | Trend Following | ✅ | ✅ | Works in any market with trends | | Mean Reversion | ✅ | ✅ | Better in stocks (more mean-reverting) | | RSI/MACD Signals | ✅ | ✅ | Same indicators, different parameters | | Bollinger Bands | ✅ | ✅ | Adjust for volatility differences | | Pairs Trading | ✅ | ✅ | Stocks: KO/PEP. Crypto: BTC/ETH | | Moving Average Crossover | ✅ | ✅ | Universal trend indicator | | Ichimoku Cloud | ✅ | ✅ | Works anywhere with enough volume | | Strategy Tournament | ✅ | ✅ | Run 10 strategies, evolve winners | | AI Iteration | ✅ | ✅ | Give AI base strategy, let it improve |

Key Differences

| Factor | Stocks | Crypto | |--------|--------|--------| | Hours | 9:30 AM - 4 PM ET | 24/7/365 | | Volatility | Lower (1-3% daily avg) | Higher (5-10%+ daily avg) | | Leverage | 2x (margin), options for more | 10-100x available | | Regulation | Heavy (SEC, FINRA) | Light (evolving) | | Data Quality | Excellent (decades) | Good (5-10 years) | | Fees | Commission-free (Alpaca) | 0.1% typical | | Short Selling | Restricted (uptick rule) | Easy (perps) | | Pattern Day Trading | $25K minimum (PDT rule) | No restriction | | Market Manipulation | Illegal, enforced | Common, less enforcement |

What Needs Adjusting for Stocks

Wider MA periods — crypto is faster, use longer MAs for stocks to filter noise

Lower leverage — 2x max for stocks vs 5-10x crypto

Market hours — need pre/after-hours data handling, overnight gaps

PDT Rule — if under $25K, limited to 3 day trades per 5 business days

Earnings events — stocks have scheduled volatility events quarterly

Sector correlation — stocks in same sector move together more than crypto

Volume patterns — stocks have predictable volume curves (open, close spikes)

Bottom Line

Yes, the core strategies transfer to stocks. The math is the same. What changes: 1. Parameters (slower timeframes for stocks) 2. Risk settings (lower leverage, wider stops) 3. Schedule (market hours vs 24/7) 4. Starting capital ($25K for day trading stocks due to PDT rule, or use swing trading to avoid it)

Best starting point for stocks: Alpaca (free, commission-free, paper trading built in, great API). Same strategies, just tune the parameters.

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15. MULTI-AGENT TRADING DESK (From Coin Bureau)

Architecture

Each agent gets a detailed "soul" (personality + priorities + instructions). Without a soul, agents produce generic output.

Agent Roles

1. Coordinator (Betty) — Manager agent. Delegates tasks, manages priorities, monitors agent health, delivers daily briefing. Never does the work itself.

2. Quant Scanner — Scans 30+ coins every 15 minutes via cron job. Uses CCXT for OHLCV data. Runs indicators (RSI, MACD, Bollinger, etc). Produces confluence score for each signal. Rules: never places trades without operator approval.

3. Researcher — Runs daily. Scans news, X/Twitter, market updates. Produces morning research brief. Focused on narratives, sectors, new listings.

4. Alert Agent — Formats signals from quant scanner into actionable alerts. Pushes to Discord channels. Includes: asset, timeframe, direction, confidence score, entry/exit suggestions.

5. Security Agent (Radar) — 24hr security audit. Checks for malicious code, prompt injections, bugs. Quality assurance on all strategy code before deployment. Non-negotiable.

6. Backtesting Agent — Validates strategies against historical data before deployment.

7. Trading Agent — Executes trades on exchange APIs. Only activated after paper trading period.

Notification Pipeline (Discord Recommended)

Private Discord server with bot admin privileges

Bot creates channels per category (signals, research, alerts, P&L)

Curated alerts — only coins/markets you care about

Much better than Telegram (categories, threads, searchable)

Key Insight From Coin Bureau

"Your output is only as good as your input." Generic prompts = generic agents. Spend time writing detailed soul descriptions for each agent. Include:

Exact responsibilities

What data to use

What format to output

What rules to follow

What to never do

Cost Reality

Claude Max ($200/mo) — unlimited for heavy agent usage

API credits alternative: $1,000-10,000/mo depending on volume

Ollama (free) — can run specific tasks locally but less smart

Strategy: use Claude for complex work, Ollama for routine scans

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16. OUR APPROACH

Phase 1: Foundation

Set up Alpaca paper trading (free)

Build data pipeline for historical OHLCV data

Implement core indicators: RSI, MACD, Bollinger, MAs, Volume

Build backtesting engine with walk-forward validation

Phase 2: Strategy Development

Backtest all 6 strategy archetypes against 5-10 years of data

Find highest Sharpe ratio + lowest max drawdown combo

Monte Carlo simulations for robustness

Paper trade top 2-3 strategies simultaneously

Phase 3: Live (After 3+ Months Paper)

Minimum position sizes

AI-driven condition analysis (use Claude to interpret market context)

Full risk management: 1% rule, daily limits, circuit breakers

Dashboard monitoring all positions, signals, performance

Key Principles

1. Paper trade until proven over 3+ months 2. Position sizing > entry signals 3. Risk management is the ONLY thing that keeps you in the game 4. No single indicator is reliable alone 5. Past performance ≠ future results 6. Start with stocks, not crypto (more data, less manipulation) 7. Account for fees, slippage, and taxes ALWAYS

Win rate: 55%, average win: $200, average loss: $100

Kelly: f = (2 × 0.55 - 0.45) / 2 = 0.325 (32.5% of account)

Half-Kelly: 16.25% — still aggressive, many pros use quarter-Kelly

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