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

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

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

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.

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

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

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

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

8. BACKTESTING

The 4 Deadly Biases

Optimization Bias (Curve Fitting): Over-tuning to historical data. Fix: minimal parameters, out-of-sample testing
Look-Ahead Bias: Using future data accidentally. Fix: strict chronological processing
Survivorship Bias: Only testing stocks that exist today. Fix: survivorship-free datasets or recent data
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

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

Michael Automates Backtesting Workflow (Proven)

His backtesting engine ($99) does this — we can replicate for free:

Get historical OHLCV data (from CCXT/Binance/Alpaca — free)
AI creates Python version of your trading strategy
Run backtest locally → get metrics
Compare with TradingView numbers to verify accuracy
If numbers don't match: Export TradingView Excel report, do trade-by-trade comparison
AI can auto-fix discrepancies by modifying the backtest engine code
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)

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

10. COMMON MISTAKES

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

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]

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:

Pick a base strategy (e.g., Super Trend, RSI+MACD, Bollinger Squeeze)
AI backtests it → gets baseline metrics (P&L, drawdown, win rate, Sharpe)
Tell AI "improve this" → it modifies parameters, adds filters, changes logic
AI creates V2, V3, V4... each iteration potentially better
Compare AI numbers with TradingView to verify accuracy
LET IT RUN OVERNIGHT — wake up to results
Score across multiple assets (BTC, ETH, SOL, SPY, AAPL) to avoid overfitting
Only keep strategies that work on 3+ different assets
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)

Create 10 different strategies (vary parameters, timeframes, indicators)
Run ALL 10 in parallel with small positions ($100 each)
Track for 1-2 weeks
Kill bottom 5 performers
Create mutations/variations of top 5
Run new tournament
Repeat — "evolution for trading strategies"
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

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

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

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:

Parameters (slower timeframes for stocks)
Risk settings (lower leverage, wider stops)
Schedule (market hours vs 24/7)
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.

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

Coordinator (Betty) — Manager agent. Delegates tasks, manages priorities, monitors agent health, delivers daily briefing. Never does the work itself.
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.
Researcher — Runs daily. Scans news, X/Twitter, market updates. Produces morning research brief. Focused on narratives, sectors, new listings.
Alert Agent — Formats signals from quant scanner into actionable alerts. Pushes to Discord channels. Includes: asset, timeframe, direction, confidence score, entry/exit suggestions.
Security Agent (Radar) — 24hr security audit. Checks for malicious code, prompt injections, bugs. Quality assurance on all strategy code before deployment. Non-negotiable.
Backtesting Agent — Validates strategies against historical data before deployment.
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

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

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

trading

Trading Skill — Complete Reference

Purpose

1. TRADING STYLES

Scalping