Trading Bots & Quant Tools 2026 Deep Dive - Lean (QuantConnect), Backtrader, Zipline, freqtrade, Hummingbot, NautilusTrader, vectorbt, Jesse

Prologue - When a Line of Code Sends an Order

The 2026 trading floor looks nothing like 1995. Back then a human shouted "Buy 100!" Today code shouts place_order(symbol="AAPL", qty=100, side="buy"). A human writes that code, backtests it, sets risk limits, then sits in front of a monitor watching the P/L curve.

Algorithmic trading is no longer a hedge fund monopoly. Over 70% of US equity volume is algorithmic, and a non-trivial share of that is retail bots. Being a quant in 2026 means you can code, you can handle data, and you can take responsibility for your own losses.

This article maps the trading bot and quant tool landscape of 2026. Open-source frameworks, data sources, broker APIs, strategy types, backtesting pitfalls, ML applications, live deployment - one map you can read in a single sitting.

Warning: this is educational. No symbol or strategy is recommended. Live trading is on you. Algorithms that drain an account to zero in five minutes happen every year.

1. The Algorithmic Trading Landscape - Who Trades What

In 2026 algorithmic trading splits into four tiers.

The territory this article covers is retail quant and prop firm entry. HFT is a different infrastructure entirely; systematic hedge funds are a hiring market.

Retail quant exploded after 2010 for three reasons. (1) Data got cheap - Polygon, Alpaca and others offer full US equities data for under USD 100/month. (2) Broker APIs opened up - Interactive Brokers, Alpaca, TradeStation went commission-free with REST APIs. (3) Open-source backtesting matured - Lean, Backtrader, vectorbt let you backtest without a PhD.

Crypto added another layer - 24/7 markets, jurisdiction-light, API-native. CCXT alone hits 100+ exchanges. Since the 2017 ICO boom, crypto has been the on-ramp for retail quants.

2. Open Source Quant Frameworks - Which One to Pick

Too much choice is a trap. Let's go one by one.

Lean (QuantConnect)

Apache 2.0, C# core with a Python API. The most production-ready open-source engine for backtesting and live trading. QuantConnect also offers SaaS hosting.

• Strengths: same code for backtest and live, multi-asset (equities, options, futures, crypto, forex), broker integrations (IBKR, OANDA, Coinbase, Binance, Tradier), data marketplace, cloud backtests.
• Weaknesses: steep learning curve, C# core makes debugging heavy, self-hosting incurs data costs.
• Use when: you are serious about going live, multi-asset.

Backtrader

GPLv3, pure Python. The most popular OSS backtester in retail. Stable since 2015.

• Strengths: clean API, rich documentation, abundant community examples, live trading via IBKR/OANDA.
• Weaknesses: maintainer activity dropped after 2020 (occasional PR merges), not vectorized (event-based, slower).
• Use when: single-asset, mid-frequency, education, following blog tutorials.

Zipline (Reloaded)

Apache 2.0, Python. After Quantopian shut down in 2020, the community maintains zipline-reloaded. The Quantopian course library still runs on it, so it remains useful as a teaching tool.

• Strengths: standard for daily-bar backtests, full factor toolkit (Alphalens, Pyfolio, Empyrical).
• Weaknesses: inefficient at intraday, US-equities-centric, Quantopian data sources gone.
• Use when: daily-bar factor research, academic backtests.

vectorbt and vectorbt PRO

Open-source (BSD-3) plus paid PRO. NumPy/Numba-based vectorized backtester. Sweeps thousands to tens of thousands of parameter combinations in seconds.

• Strengths: overwhelming speed, interactive in Jupyter, perfect for hyperparameter sweeps.
• Weaknesses: event-based logic (partial fills, slippage models) is awkward, PRO is expensive (USD 1000+/year).
• Use when: idea exploration, fast parameter sweeps, research phase.

NautilusTrader

GPLv3 plus commercial license, Python with a Rust core. A newer entrant aiming at both performant backtests and live trading. Grew quickly 2023-2026.

• Strengths: Rust-backed speed, event-based with accurate simulation, async/await, options and crypto support.
• Weaknesses: still maturing, documentation uneven.
• Use when: high-frequency or serious live deployments.

freqtrade

GPLv3, Python. Crypto-only OSS bot. Community-run since 2017 with active Discord and Telegram.

• Strengths: fast start, Telegram bot integration, hyperopt, FreqAI (ML integration), 100+ exchanges via CCXT.
• Weaknesses: crypto only, strategy library quality varies (user contributions).
• Use when: starting crypto auto-trading, Telegram control.

Jesse

MIT, Python. Another crypto bot framework. More modern API than freqtrade, with a built-in GUI.

• Strengths: clean API, optimizer, live dashboard, Docker-friendly.
• Weaknesses: smaller community, fewer exchanges than freqtrade.
• Use when: freqtrade alternative, want a cleaner Python codebase.

Hummingbot

Apache 2.0, Python. Built by Coinalpha for market making and arbitrage. CEX and DEX support.

• Strengths: PMM (Pure Market Making) built-in, DEX support (Uniswap, dYdX), V2 script framework.
• Weaknesses: MM is capital-inefficient, slippage risk.
• Use when: liquidity provision (maker rebates), pair arbitrage.

Octobot, Cryptohopper, 3Commas

Managed SaaS bots (set up via GUI without code). Cryptohopper (Netherlands), 3Commas (Estonia) are subscription-based; Octobot is open-source plus hosting.

• Strengths: no-code, instant start, mobile apps.
• Weaknesses: black-box strategies (template-dependent), API keys handed to SaaS - security risk, subscription fees.
• Use when: cannot code yet but want auto-trading - with eyes open about the trade-offs.

bt, Catalyst, backtesting.py

Smaller libraries. bt is portfolio-backtest friendly, Catalyst is Enigma's crypto backtester (largely abandoned since 2019), backtesting.py is a lightweight single-asset backtester.

• Use when: bt for multi-asset rebalancing strategies, backtesting.py for quick prototypes.

3. Decision Tree - What Should You Pick

Selection criteria, condensed.

1. Single stock, fast backtest only: backtesting.py or vectorbt.
2. Starting crypto auto-trading: freqtrade (battle-tested) or Jesse (modern).
3. Market making, DEX: Hummingbot.
4. Multi-asset, going live: Lean (QuantConnect) or NautilusTrader.
5. Daily-bar factor research (academic): Zipline plus Alphalens.
6. Hyperparameter sweep: vectorbt.
7. No code: 3Commas or Cryptohopper (with risk awareness).

4. Data Sources - Garbage In, Garbage Out

A great algorithm on bad data produces bad results. Major 2026 vendors.

US Equities and Options

• Polygon.io - the most popular. Real-time and historical, options, crypto, forex. From USD 29/month, full options at USD 199/month.
• Alpaca Markets Data - broker plus data bundle. Free IEX data, paid SIP full feed.
• IEX Cloud - shrinking after a 2022 policy change. Mostly legacy users.
• Tradier - broker plus data, strong options.
• Nasdaq Data Link (formerly Quandl) - alternative data (VIX futures expiry etc.), some free.
• Yahoo Finance and yfinance - free but unofficial, harsh rate limits, missing values. Learning only.
• Twelve Data, Finnhub, Marketstack - newer vendors, attractive pricing, but data quality requires validation.
• Bloomberg Terminal and Refinitiv - institutional, USD 2000+/month. Effectively irrelevant for retail.

Crypto

• CCXT - unified Python/JS library for 100+ exchanges. De facto standard. Free.
• Kaiko and Coin Metrics - institutional, vetted.
• CryptoCompare and Glassnode - on-chain data included.

Alternative Data

• SEC EDGAR - filings, free.
• FRED (Federal Reserve) - macro indicators, free.
• OpenFIGI - security identifiers.
• Estimize and Visible Alpha - analyst consensus.

Practical tip: before going live, pull the same data from two vendors and verify they agree. If one is missing, the other fills the gap. If one ships bad prices (missed corporate actions etc.) your backtest lies to you.

5. Broker APIs - Who Accepts Your Orders

A strategy that backtests well is dead in the water if no broker will execute it.

US and Global

• Interactive Brokers (IBKR) - global number one, nearly every asset class. Python via ib_insync or the official TWS API. Caveat: getting your first order through takes days.
• Alpaca - commission-free, REST/WebSocket, free paper trading. The de facto retail algo on-ramp. US equities, options, crypto.
• Tradier - REST API, strong on options.
• TradeStation and Charles Schwab (formerly TD Ameritrade) - traditional, less developer-friendly APIs.
• OANDA - forex specialist, clean REST API.

Korea

• Kiwoom OpenAPI+ - Windows COM-based (old tech), still the most-used retail API. Wrappers like PyKiwoom are active.
• Korea Investment KIS Developers - REST API launched in 2022, modern and clean. Quickly emerging as the Kiwoom alternative.
• eBest xingAPI - another option.
• NHN algorithmic trading cafe - the largest Korean retail quant community.

Japan

• SBI Securities API - Japan's number one brokerage. External APIs are limited.
• Rakuten Securities Marketspeed II RSS - Excel/COM-based.
• Matsui Securities - small but API-friendly.
• GMO Coin, bitFlyer, Coincheck - Japanese exchange crypto APIs.

Crypto

• Binance, Coinbase, Kraken, Bybit, OKX - global majors. All REST/WebSocket.
• Upbit, Bithumb - Korean exchanges. KYC required, strict rate limits.
• dYdX, Hyperliquid, Vertex - DEX perpetuals, exploded in growth 2024-2025.

Practical tip: paper-trade for at least one month before going live. Paper vs live always diverges on slippage and fill rate.

6. Strategy Families - What Will You Trade

Five families cover most retail strategies.

Trend Following

If prices move one way, bet in that direction. The core of every systematic fund except Renaissance.

• Signals: moving-average crossovers (MA(50) vs MA(200)), ADX, channel breakouts, Donchian Channel.
• Timeframe: daily to monthly.
• Strengths: simple, captures major trends.
• Weaknesses: frequent false signals (whipsaw), losses in choppy markets.
• Classic book: Andreas Clenow, Following the Trend.

Mean Reversion

The premise that prices revert to their mean.

• Signals: Bollinger Band breaks, RSI overbought/oversold, z-score.
• Timeframe: minutes to days.
• Strengths: frequent trades, can be market-neutral.
• Weaknesses: "knife catching" - destroyed by sustained trends.

Statistical Arbitrage

When the spread between correlated pairs diverges, trade it. Pair trading is the canonical example.

• Signals: cointegration, spread z-score.
• Strengths: market-neutral.
• Weaknesses: huge losses when correlation breaks, data cleaning is painful.

Market Making

Quote both sides of the book and capture the spread. Hummingbot PMM lives here.

• Strengths: maker rebates, statistically stable returns.
• Weaknesses: capital tied up, adverse selection when markets run one way.

Statistical and ML Alpha

Feature engineering plus ML predicts short-term moves. The core of Two Sigma, Renaissance and similar.

• Signals: hundreds of features (prices, volume, macro, news, social) plus tree-based models or deep learning.
• Strengths: still produces alpha when classic signals fade.
• Weaknesses: extreme overfit risk, data infra cost, "no one knows why it works."

Latency Arbitrage

Microsecond gaps between exchanges or routing paths. Effectively impossible for retail - requires colocation and dedicated lines.

7. Backtesting Pitfalls - Seven Lies of a Pretty Equity Curve

The point of backtesting is learning not to trust the result.

1) Survivorship Bias

If your dataset excludes delisted tickers, every strategy looks great. Always use a dataset that includes delisted history. CRSP is the standard; retail uses Norgate Data or Polygon's historical universe.

2) Look-Ahead Bias

Future data leaks into your signal computation. Example: using the daily close to enter that same day intraday. Separate signal timestamps from order timestamps.

3) Overfitting / Curve Fitting

You picked the best combo out of 100 parameter sets and now you have Sharpe 3.5 - you fitted noise. Mitigations: out-of-sample validation, walk-forward analysis, parameter robustness checks.

4) Ignoring Slippage and Fees

The fill price equals the signal price? No chance. Market orders eat the top of the book and you average worse. Limit orders miss. Minimum: add 0.05-0.1% per trade for fees and slippage. For small caps or crypto, go more conservative.

5) Data Snooping

Test hundreds of strategies on the same data and one will win by chance. Bonferroni correction or deflated Sharpe ratio.

6) Ignoring Volume Constraints

If your strategy orders more than 10% of average daily volume, you move the price. The backtest does not know that. Cap positions at 1-5% of ADV.

7) Single-Era Data

Backtesting only on 2020-2023 means you only saw a bull market. At least ten years, ideally including 2008 and 2020 crashes.

Golden rule: when you see a backtest result, ask "if this were real, why has no one already arbitraged it away?" If you cannot answer, that alpha is a backtest artifact.

8. Walk-Forward Analysis - Overfit Defense

Serious backtests are walk-forward.

1. In-sample window (e.g., 2015-2018): optimize parameters.
2. Out-of-sample window (e.g., 2019): evaluate with those parameters.
3. Slide one year at a time - train on 2016-2019, evaluate on 2020, ...
4. Take OOS performance only as the real performance number.

Even this can be over-optimistic (the walk-forward window size is also a tuneable parameter). The safer route is averaging across multiple seeds, symbols, and time periods.

9. ML Applications - Trees, Deep Learning, Time-Series Foundation Models

ML has been in trading for 30 years, but the 2020s changed things.

Classical ML

• scikit-learn - the basics. Logistic regression, random forests, SVM.
• XGBoost, LightGBM, CatBoost - the gradient boosting trio. Still kings on tabular data.
• Feature engineering is 90%: prices, volume, technicals (RSI, MACD), macro, sentiment. Lopez de Prado, Advances in Financial Machine Learning, is the textbook.

Deep Learning

• PyTorch and TensorFlow - LSTM, Transformer, CNN over price time series.
• Transformer-based price forecasting papers exploded (2021-2025).
• Reality: tree models often match or beat. Deep learning shines when combining unstructured data (news, images).

Reinforcement Learning

• stable-baselines3, Ray RLlib - PPO, SAC-based trading agents.
• Theoretically attractive - agent learns optimal policy directly.
• Reality: reward shaping is hard, sample efficiency is poor. Real live deployments are rare but research is active.

Time-Series Foundation Models (the 2024-2026 wave)

• TimesFM (Google, 2024) - 200M-1B parameter time-series transformer, zero-shot forecasting.
• Chronos (Amazon, 2024) - T5-based time-series encoder.
• Lag-Llama (ServiceNow + Mila, 2024) - Llama architecture applied to time series.
• Moirai (Salesforce) - a universal time-series model.
• Reality: strong on general time series; financial series have low SNR so vanilla application is limited. Fine-tuning is the key.

Classical Time Series

• Prophet (Meta) - explicit trend and seasonality decomposition.
• NeuralProphet, NeuralForecast - neural extensions.
• TimeGPT (Nixtla, 2023) - hosted API for zero-shot forecasts, commercial.

Practical advice: start with XGBoost as a baseline. If that does not work, deep learning will not save you. If it does, then try LSTM and transformers. RL and foundation models earn another build only after baselines are working.

10. Risk Management - More Important Than Alpha

A strategy averaging 5% monthly with one -50% drawdown is finished. Risk management beats alpha.

Core Metrics

• Sharpe Ratio = (return - risk-free) / std. Above 1.0 is good; above 2.0 should be suspect.
• Sortino Ratio = Sharpe but only downside volatility. More honest.
• Calmar Ratio = annual return / max drawdown. Above 1.0 is solid.
• Max Drawdown (MDD) = peak-to-trough max decline. Above 30% is psychologically unbearable.
• Win Rate vs Avg Win/Loss = 50% win rate with 2:1 reward/risk is still a winner.
• Profit Factor = gross profit / gross loss. Above 1.5 is meaningful.

Position Sizing

• Fixed fraction - X% of equity each. Simple.
• Volatility targeting - inverse to instrument vol. Natural diversification.
• Kelly Criterion - mathematically optimal bet size. f* = (bp - q) / b. In practice Half-Kelly or Quarter-Kelly is sensible.
• Risk parity - equal risk contribution per name.

Limits

• Position limits - one name caps at 5-10% of equity.
• Sector/country limits - no sector over 30%.
• VaR / CVaR - daily 99% confidence loss bound.
• Stops - trailing or fixed. Useless across gaps, however.

11. Live Deployment - From Backtest to Real Orders

A strategy can backtest well and the road to live is still long.

Infrastructure

• Local PC - keep it on 24/7, risk of outages and internet drops. Beginner only.
• VPS (DigitalOcean, Hetzner, Linode) - USD 5-20/month. The answer for most retail bots.
• AWS Lambda + EventBridge - daily strategies, cron-driven order once per day.
• AWS EC2 / GCP Compute - intraday, needs to be always on.
• Colocation - HFT only, irrelevant for retail.

Monitoring

• Alerts - fills, loss-limit breaches, system down: push to Telegram, Slack, Discord.
• Dashboards - Grafana plus Prometheus, or a built-in (freqtrade UI).
• Logs - persist every order, fill, and signal. Needed for debug and taxes.

Failsafes

• Kill switch - automatic liquidation and bot halt at daily loss limit.
• Manual toggle - a single human command must be able to stop the bot.
• Double-confirm - re-validate large orders before sending.
• Connection drop handling - decide ahead of time what happens when the broker API drops (usually reduce-only mode).

12. The First 100 Hours - Where to Start

Do not go live from day one. Suggested ladder.

1. Hours 1-10: pull daily bars with yfinance, code an SMA crossover backtest in plain pandas.
2. Hours 10-30: re-implement the same strategy in backtesting.py or vectorbt, add slippage and fees.
3. Hours 30-60: open an Alpaca paper account and send mock orders via REST API.
4. Hours 60-100: paper trade for a month. Measure backtest vs paper divergence.

People who finish those 100 hours go live for real. Around 90% of those who skip ahead lose money. Simple arithmetic.

13. Korean Retail Quant - Kiwoom, KIS, NHN

The Korean retail quant landscape.

• Kiwoom OpenAPI+ - the most-used. Downsides: Windows-only (COM), 32-bit Python constraints. Workarounds via PyKiwoom and KOA Studio.
• Korea Investment KIS Developers - REST API launched 2022. Modern and multi-platform. Quickly going mainstream.
• eBest xingAPI - strong for options and futures.
• NH NamuH API - late entrant.
• NHN algorithmic trading cafe - the largest community, actively shares code.
• Tax issues: short-term algo trading complicates tax filing. Capital gains and financial investment income tax changes require tracking.

14. Japanese Retail Quant - SBI, Rakuten, GMO

Japan looks a bit different.

• SBI Securities - the number-one brokerage. APIs are restricted, little external exposure.
• Rakuten Securities - Marketspeed II RSS (Excel/COM-based), the Japanese retail algo standard.
• Matsui Securities - small but API-friendly.
• GMO Coin, bitFlyer, Coincheck - Japanese exchange crypto APIs.
• Nomura Quant (Prime) - institutional, unrelated to retail.
• Local context: NISA and iDeCo tax incentives, margin trading regulations, FSA oversight.

15. Regulation and Liability - What to Know

Algo trading does not sit above the law.

• United States: the SEC oversees RIA (investment adviser registration). Trading only your own money does not require registration. Managing other people's money does. FINRA oversees broker-dealers.
• Market manipulation prohibitions: spoofing, layering, wash trading carry criminal liability. Even unintentional patterns from an algo bot are risky if they look like spoofing.
• Korea: the FSC and FSS oversee. Personal algo trading is effectively unrestricted, but suspicious price manipulation triggers investigation.
• Japan: the FSA oversees, with a similar structure.
• Taxes: keep every trade record for 5-7 years. Short-term trades may be taxed as ordinary income, others as capital or financial investment income.

16. Famous Quant Firms - Who's Who

The industry map.

• Renaissance Technologies - founded by Jim Simons. The Medallion Fund averaged 66% annually for 30 years. Closed to outside capital.
• Two Sigma - David Siegel and John Overdeck. Heavy ML focus.
• D.E. Shaw - David Shaw. Multi-strategy, alma mater of Jeff Bezos.
• Citadel - Ken Griffin. Multi-strategy, both market making and hedge funds.
• AQR - Cliff Asness. Academic factor investing.
• Jane Street - options market maker. Famous for OCaml; JS Capital reaches Korea too.
• Optiver, IMC, Flow Traders, DRW - European-rooted prop/MM firms.
• Hudson River Trading, Jump Trading, Tower Research - US HFTs.
• Millennium, Point72 - multi-manager hedge funds.

Hiring looks for: math/stats/CS PhDs or IMO/ACM medalists, top-tier coding, ruthless interviews on pairs trading and options pricing.

17. Recommended Learning Resources - Books, Courses, Papers

For retail quants going deeper.

Books

• Ernest Chan, Quantitative Trading - a good starter.
• Lopez de Prado, Advances in Financial Machine Learning - the ML textbook.
• Andreas Clenow, Following the Trend, Stocks on the Move - trend following.
• John Hull, Options, Futures, and Other Derivatives - the derivatives standard.
• Sinclair, Volatility Trading - for options traders.
• Aronson, Evidence-Based Technical Analysis - defense against backtesting traps.

Courses

• QuantConnect Bootcamp - start with Lean.
• MIT 6.S979 Quantitative Methods in Finance - academic foundation.
• Coursera Stanford Financial Engineering and Risk Management.
• Hudson and Thames courses - Lopez de Prado follow-ups.

Papers and Research

• Journal of Finance, Journal of Financial Economics - academic.
• SSRN - working papers, free.
• arXiv q-fin - ML plus finance.

Communities

• r/algotrading - the main retail hub.
• QuantConnect Forum - Lean-specific.
• Wilmott Forums - the classic.
• Quantitative Trading SE - Q&A.

18. A Workable Weekly Cadence - One Loop

A solo full-time retail quant week, roughly.

• Mon: review last week's P/L, scan the macro, tidy research notes.
• Tue: backtest a new idea in Jupyter plus vectorbt.
• Wed: walk-forward validation on the promising ones.
• Thu: monitor paper trading results, health-check live bots.
• Fri: code cleanup, commit, push, plan next week.
• Weekend: read books and papers, get distance from the market.

Staring at charts daily drives you crazy. The goal is automate, monitor, let the human make decisions only.

19. Common Mistakes - Five Ways to Blow Up in Year One

The most common breakages in the first 12 months of retail quant.

1. Blind faith in backtests - a 100x backtest down 90% live. Usually overfit.
2. Ignored fees and slippage - the backtest was free; live bleeds 0.1% each trip.
3. Position too big - 20% of equity on one trade. One miss and recovery is impossible.
4. Leverage abuse - 5x leverage in a vol spike, liquidated.
5. Manual override - the bot is losing so you intervene - you have lost the point of automation.

20. Five-Year Outlook - Where Things Go

A 2026-2030 forecast.

• Time-series foundation models become mainstream - reasonable forecasts from less data.
• DEX and DeFi trading bots get more sophisticated - frameworks like Hummingbot V2.
• AI agent traders - Claude or GPT chaining research, code and execution. Trust is still far away.
• Data costs fall further - free or low-priced data competes harder.
• More regulation - SEC, FSC, FSA guidance specific to AI-driven bots.
• Latency arb gets harder - HFT competition intensifies, retail entry further away.
• Long-term value investing vs algo trading polarizes - the middle disappears.

21. Closing - What Matters More Than Code

Learning algorithmic trading teaches you two things.

First, markets pretend to be efficient but are not completely so. Small inefficiencies exist. They just often cost more to capture than the alpha is worth.

Second, winners are not the smartest strategies but the surviving ones. 100% one year then -95% the next does not average to zero - it averages to -90% (geometric mean). Preservation beats alpha.

The tools, strategies and theories in this article are just tools. Results come from discipline, risk limits and humility before the market. Remember every day that a sleeping bot could be bleeding losses, and start only when you have the capital and the temperament to accept that possibility.

Good luck. And do not forget - you can earn alpha, but you cannot borrow risk.

22. References

• QuantConnect Lean - github.com/QuantConnect/Lean
• Backtrader - github.com/mementum/backtrader
• Zipline Reloaded - github.com/stefan-jansen/zipline-reloaded
• vectorbt - github.com/polakowo/vectorbt
• NautilusTrader - github.com/nautechsystems/nautilus_trader
• freqtrade - github.com/freqtrade/freqtrade
• Jesse - github.com/jesse-ai/jesse
• Hummingbot - github.com/hummingbot/hummingbot
• Octobot - github.com/Drakkar-Software/OctoBot
• backtesting.py - github.com/kernc/backtesting.py
• CCXT - github.com/ccxt/ccxt
• Polygon.io - polygon.io
• Alpaca - alpaca.markets
• Interactive Brokers TWS API - interactivebrokers.github.io
• Korea Investment KIS Developers - apiportal.koreainvestment.com
• TimesFM - github.com/google-research/timesfm
• Chronos - github.com/amazon-science/chronos-forecasting
• Lag-Llama - github.com/time-series-foundation-models/lag-llama
• TimeGPT (Nixtla) - docs.nixtla.io
• Lopez de Prado, Advances in Financial Machine Learning - wiley.com
• Ernest Chan - epchan.com
• QuantConnect Bootcamp - quantconnect.com/learning
• Hudson and Thames - hudsonthames.org
• r/algotrading - reddit.com/r/algotrading