The Simplest Way To Start Day Trading In 2026 (Full Course)

High-level summary

A beginner-to-algorithmic day trading course teaching a liquidity-based intraday strategy (recommended for S&P 500 futures — ES) and how to automate it. The presenter claims >$800,000 made in the markets last year and argues algorithms will be essential by 2026.

Core thesis: markets move to liquidity (clusters of stop-loss and breakout orders). Identify institutional liquidity levels (untested highs/lows, session highs/lows, and the New York open 08:00–08:15 EST midpoint) and trade the market’s reaction with clear rules. Automate validated rule-based strategies to remove emotion, ensure you never miss setups, scale, and gather sufficient statistical evidence via backtests.

Quote (presenter claims)

“95% of traders fail.”

Assets, tickers and instruments mentioned

• Futures: ES (S&P 500 futures, primary recommendation), NQ (NASDAQ futures).
• Stocks: Apple, Tesla, Amazon (examples only).
• Forex: currency pairs (example: JPY vs GBP).
• Commodities / Crypto: gold, oil, Bitcoin (crypto framed as better for investing).
• Instruments / platforms: stocks, forex, futures, crypto; TradingView (Pine Script); prop/funded accounts; brokers (example: Apex); Discord; Manis AI (AI tool for coding).

Key market and operational numbers, timelines, rules

• Session times (EST):
  • Asia: 19:00–04:00
  • London: 03:00–12:00
  • New York session starts 08:00; U.S. stock open 09:30; NY close ~17:00
  • Most important intraday window: 08:00–08:15 (first 15‑minute candle / opening range); 09:30 (stock open) is also critical.
• ES point value: 1 point = $50 (so a 10‑pt move = $500 per contract).
• Pattern Day Trading rule: accounts < $25,000 are restricted for frequent day trades (cited as a reason stocks are hard for small accounts).
• Typical timeframes:
  • Primary: 15‑minute (structure) and 5‑minute (execution)
  • Entry refinement: 1‑minute
  • Swing algorithm example: 1‑hour timeframe
• Risk-to-reward targets: typically 1:3, often 1:4; sometimes 1:5. Example setups:
  • 5‑pt stop / 15–16.5‑pt target (1:3)
  • 10‑pt stop / 40‑pt target (1:4)
• Risk per trade rule: never risk more than 1–2% of account per trade.
• Industry/macro claims:
  • Estimated >70% of stock market trading volume executed by algorithms (presenter’s assertion).
  • Renaissance/Medallion cited: historically high returns used as an example of institutional algorithmic edge.

Methodology — step-by-step framework

1. Chart setup and timeframes
   • Use candlestick charts.
   • Day-trader primary timeframes: 15‑minute for structure, 5‑minute for execution; 1‑minute for fine entry confirmation.
   • Optional cosmetic changes (e.g., candlestick colors) to reduce emotional reaction.
2. Identify session liquidity levels
   • Mark untested highs and lows for Asia, London, and New York sessions.
   • Draw the New York open 08:00–08:15 15‑minute box and mark its midpoint (midpoint often acts as a bounce point).
   • Use a session-range indicator to automate session ranges (example: “Asian session range” by Rob Minty on TradingView).
3. Trade only the three primary setups (rules emphasized; execute mainly on ES/NQ)
   • Break-and-Retest (primary)
   • Bounce (untested session low)
   • Rejection (untested session high)
4. Execution, sizing and risk rules
   • Always program/define stop-loss and position sizing based on the 1–2% risk rule.
   • Use fixed, predefined profit targets and stop placement; keep winners larger than losses.
5. Validation and automation
   • Backtest strategy on historical data to accumulate thousands of trades and determine true edge.
   • Automation paths: build from scratch, use AI-assisted coding tools, or deploy proven pre-built algorithms (recommended for most retail traders).

The three primary setups (detailed)

1) Break-and-Retest (primary)

• Mark the 08:00–08:15 (NY opening) 15‑minute box on the 15‑min chart.
• At 09:30, if price breaks out of the box, wait for a retest of the midpoint (not necessarily the top/bottom).
• Confirm strength on 5‑min and 1‑min charts; enter on retest with a stop below the zone.
• Example sizing: 5‑pt stop, target ~15–16.5 pts (1:3).

2) Bounce

• Price falls to an untested session low; look for a 1‑minute tap then consistent closes above the level on 5‑min.
• Enter after confirming the bounce.
• Example sizing: ~10‑pt stop, ~40‑pt target (1:4).

3) Rejection

• Price taps an untested session high and fails to close above it.
• Use 5‑min and 1‑min confirmation; enter shorts on rejection.
• Targets/stops similar to bounce setups (e.g., 10‑pt stop / 40‑pt target).

Execution and sizing

• Predefine stop-losses and profit targets for each setup.
• Position size so that loss per trade ≤ 1–2% of account.
• Use consistent sizing rules; avoid ad-hoc position changes during live trading.

Risk management rules (explicit)

• Never risk more than 1–2% of account on a single trade.
• Always use a stop-loss.
• Let winners run; don’t cut winners early.
• Do not revenge trade — take breaks and journal after losses.

Validation and automation

• Backtest before live trading. Accumulate thousands of trades to validate an edge.
• Example backtest lesson: a tested MACD strategy showed ~35% win rate and net negative over recent years — demonstrates the need to backtest.
• Three automation paths:
  1. Build from scratch (costly: tens to hundreds of thousands of dollars).
  2. Use AI tools to help code/test (example: Manis AI for Pine Script).
  3. Deploy a proven pre-built algorithm (recommended for most retail traders).

Presenter’s performance claims, backtests and bots

• Presenter claims to have built three algorithms based on the liquidity strategy and to have substantial backtesting and live funded account usage:
  1. ES scalping bot (1‑minute style): backtest shown over ~3,134 trades; profitable across multiple years with small losses and larger winners (examples of 20–50 pt winners).
  2. Prop-firm scalping bot (NQ, 5‑minute): presented as ~80% win rate over five years (suited for prop firm challenges).
  3. Swing/hourly bot (1‑hour): running since 2021, holds 1–5 hours sometimes overnight; backtest over ~5 years with consistent profitability (example single trade wins cited).
• Client-reported results (screenshots): examples such as $7,500 in a day, $1,200, $265; weekly examples $860 and $1,240 using small micro contracts.
• Presenter plans live-broker demo (Apex) and claims algorithms run on multiple live accounts.

Explicit recommendations, cautions and practical advice

• Start market: ES (S&P 500 futures) for liquidity and regulated U.S. futures brokers.
• If account < $25,000, stocks are constrained by the PDT rule — consider futures or prop/funded accounts.
• Learn manual execution first to understand setups; then validate via backtesting before automating.
• Recommended path for most serious traders: deploy a proven pre-built algorithm rather than building from scratch.
• Behavioral cautions: don’t overtrade, don’t revenge trade, follow risk rules strictly.
• Automation requires seriousness and capital; it’s not for everyone.

Tools and technology mentioned

• TradingView (Pine Script) — charting and backtesting.
• Indicator: “Asian session range” by Rob Minty (TradingView).
• Manis AI — used to auto-generate Pine Script strategies.
• Broker example: Apex (used in demo).
• Prop/funded accounts and Discord — used to deploy and share results.

Disclosures and qualification statements

• Presenter frames automation as not for everyone and requests seriousness and willingness to invest.
• No formal “not financial advice” phrase was recorded in the transcript.
• Performance claims (personal >$800k last year; algorithm/backtest screenshots) are promotional and should be independently verified before committing capital.
• Presenter emphasizes that thousands of backtested trades are needed to validate an edge.

Concise implementation checklist

• Learn candlestick reading; use 15‑min (structure) + 5‑min (execution) + 1‑min (entries).
• Mark session levels: untested highs/lows for Asia, London, New York; draw 08:00–08:15 NY opening range box and midpoint.
• Trade only the three setups: break‑and‑retest of midpoint, bounce off untested low, rejection at untested high.
• Use discrete entry/stop/target rules; aim for ≥1:3 R:R; risk ≤1–2% per trade.
• Backtest thoroughly (thousands of trades) before going live.
• Choose automation path: build, AI-assisted, or deploy pre-built algorithm (recommended).

Presenters / sources referenced

• Main presenter: teacher and algorithm developer (unnamed in transcript; claims personal P&L and that he built three algorithms).
• Presenter’s “godfather”: ex-Goldman Sachs trader (30 years) who taught the liquidity framework.
• Rob Minty: TradingView indicator author (“Asian session range”).
• Manis AI (Manis): AI coding assistant for Pine Script.
• Renaissance Technologies / Medallion Fund: cited as an institutional algorithmic example.
• Platforms / services: TradingView, Pine Script, Apex broker, Discord, funded/prop accounts.