Tushare Data
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
Pricing completo de opciones europeas y americanas. An agent skill from gauss314/skills.
$ npx skills add gauss314/skills --skill option-pricing -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install gauss314/skills option-pricing --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/option-pricing .claude/skills/option-pricing && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .claude/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/gauss314/skills/tree/main/skills/option-pricingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add gauss314/skills --skill option-pricing -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install gauss314/skills option-pricing --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/option-pricing .agents/skills/option-pricing && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .agents/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gauss314/skills --skill option-pricing -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install gauss314/skills option-pricing --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/option-pricing .cursor/skills/option-pricing && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .cursor/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/gauss314/skills.git --path skills/option-pricing--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add gauss314/skills --skill option-pricing -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install gauss314/skills option-pricing --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/option-pricing .gemini/skills/option-pricing && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .gemini/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install gauss314/skills option-pricingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add gauss314/skills --skill option-pricing -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/option-pricing .github/skills/option-pricing && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .github/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add gauss314/skills --skill option-pricing -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install gauss314/skills option-pricing --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/gauss314/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/option-pricing .opencode/skills/option-pricing && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "option-pricing" agent skill from https://github.com/gauss314/skills/tree/main/skills/option-pricing into .opencode/skills/option-pricing/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "option-pricing", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
option-pricingPricing completo de opciones europeas y americanas. An agent skill from gauss314/skills.
Option Pricing is an agent skill from gauss314/skills. Pricing completo de opciones europeas y americanas. 9 metodos: Black-Scholes, Binomial CRR, Trinomial, Monte Carlo (antithetic) + Longstaff-Schwartz, Bjerksund-Stensland 2002 / BAW (American closed-form), Heston 1993 (vol estocastica, sonrisa via Fourier), Bates 1996 (Heston + Merton jumps, crash risk), greeks (BS), implied vol, P(ITM) y P(Profit). Disenado para backtesting: cada funcion es flat Python vectorizado con numpy (sin abstracciones), usa math.erfc (no scipy). BS 2.4 us/op, BS2 3.6 us, Heston 400 us…
Its SKILL.md is about 4.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts, reference files and assets (for example `assets/defaults.json`, `assets/validation_cases.json` and `references/REFERENCE.md`).
It sits in Business, Finance & HR, covering Trading and backtesting. It works with Python and NumPy. The repository describes itself as: Financial market data consumption skills for claude code and AI agents. The licence is MIT.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 5156f81. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 1 file in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Option Pricing loads about 4.7k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 160 tokens; SKILL.md has 1,285 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from gauss314/skills at commit 5156f81, republished under its MIT licence (© gauss314). 1,285 words, ~4,663 tokens.
.claude/skills/option-pricing/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Pricing de opciones para backtesting y analisis. 5 metodos implementados, todos en flat Python + numpy (sin dependencias externas pesadas, sin abstracciones, sin clases). Cada funcion es ~100 lineas o menos y acepta escalares.
Performance objetivo (medida en este skill):
Para la teoria detallada de cada metodo, ver references/REFERENCE.md.
# 1. Black-Scholes (europea)
py scripts/option_pricing.py bs --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
# 2. Binomial CRR (europea o americana)
py scripts/option_pricing.py binomial --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 --style american
# 3. Trinomial (europea o americana)
py scripts/option_pricing.py trinomial --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
# 4. Monte Carlo (europea, antithetic variates)
py scripts/option_pricing.py mc --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 --paths 200000
# 5. Longstaff-Schwartz (americana via MC)
py scripts/option_pricing.py lsm --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 \
--style american --paths 100000 --steps 50
# 6. Barone-Adesi-Whaley (americana closed-form)
py scripts/option_pricing.py bs2 --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 --q 0.04 \
--style american
# 7. Greeks analiticos (BS)
py scripts/option_pricing.py greeks --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
# 8. Implied volatility
py scripts/option_pricing.py iv --S 100 --K 100 --T 0.25 --r 0.05 --price 4.62
# 9. P(ITM) y P(Profit) bajo medida risk-neutral Q
py scripts/option_pricing.py pitm --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
py scripts/option_pricing.py pitm --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 --premium 4.62
# 10. Superficie de precios across strikes
py scripts/option_pricing.py surface --S 100 --T 0.25 --r 0.05 --sigma 0.20 \
--K-min 80 --K-max 120 --K-step 5
# 11. Heston 1993 (vol estocastica, sonrisa via Fourier)
py scripts/option_pricing.py heston --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 \
--v0 0.04 --kappa 2.0 --theta 0.04 --sigma_v 0.3 --rho -0.5
# 12. Bates 1996 (Heston + Merton jumps, captura crash risk)
py scripts/option_pricing.py bates --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20 \
--v0 0.04 --kappa 2.0 --theta 0.04 --sigma_v 0.3 --rho -0.5 \
--lam 1.0 --mu_J -0.05 --sigma_J 0.10
# 13. Comparar todos los metodos aplicables
py scripts/option_pricing.py all --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
# Validar contra casos de assets/validation_cases.json
py scripts/option_pricing.py validate
# Benchmark de todos los metodos
py scripts/option_pricing.py bench --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20skills/option-pricing/
├── SKILL.md # Este archivo (guia rapida)
├── references/
│ └── REFERENCE.md # Teoria completa de los 5 metodos
├── assets/
│ ├── defaults.json # Parametros default para el CLI
│ └── validation_cases.json # Casos de test (Hull Examples + extra)
└── scripts/
└── option_pricing.py # CLI con 15 modos + validate + bench| Flag | Default | Descripcion |
|---|---|---|
--S | 100.0 | Spot price del subyacente |
--K | 100.0 | Strike |
--T | 0.25 | Tiempo a maturity en anos (0.25 = 3 meses) |
--r | 0.05 | Tasa libre de riesgo (continua anual) |
--q | 0.0 | Dividend yield continuo anual |
--sigma | 0.20 | Volatilidad anualizada |
--type | call | call o put |
--style | european | european o american |
--steps | 500 | Pasos del tree / pasos temporales de LSM |
--paths | 100000 | Paths de Monte Carlo |
--seed | 42 | Seed para reproducibilidad |
--antithetic | True | Activar variates antitetic (MC) |
--json | False | Output en JSON en vez de tabla |
Defaults se cargan de assets/defaults.json (modificables).
Inputs del benchmark (ATM call, mismos valores para todos los metodos):
S=100, K=100, T=0.25, r=0.05, q=0, sigma=0.20. Medido con 2000 reps (o
menos para metodos lentos), Windows 11. No asumido — medido con
time.perf_counter() sobre la funcion expuesta por el skill.
| Metodo | Time complexity | us/op (medido) | ops/sec | Estilo | Error tipico |
|---|---|---|---|---|---|
Black-Scholes bs | O(1) | 2.4 us | 419k/s | European | 0 (closed) |
P(ITM) pitm | O(1) | 1.1 us | 908k/s | Ambos | 0 (closed N(d2)) |
Greeks (BS) greeks | O(1) | 3.9 us | 257k/s | European | 0 (closed) |
BS2/BAW bs2 | O(1) | 3.6 us | 276k/s | American | <1% vs binomial N=2000 |
IV solve iv | O(log(1/eps) * 1 opcion) | 82 us | 12k/s | Ambos | 1e-7 |
Heston heston | O(N_GL) ~ O(1) | 398 us | 2.5k/s | European | <0.1% |
Binomial N=500 binomial --steps 500 | O(N^2) | 5.6 ms | 178/s | Ambos | ~0.5% |
Bates bates (15 term) | O(15 * N_GL) | 6.2 ms | 160/s | European | <0.5% |
MC paths=10k mc --paths 10000 | O(paths) | 1.3 ms | 788/s | European | ~1% (stderr) |
Trinomial N=500 trinomial --steps 500 | O(N^2) | 9.4 ms | 107/s | Ambos | ~0.3% |
MC paths=100k mc --paths 100000 | O(paths) | 5.6 ms | 177/s | European | ~0.1% (stderr) |
Binomial N=2000 binomial --steps 2000 | O(N^2) | 31 ms | 32/s | Ambos | ~0.1% |
LSM paths=10k lsm | O(paths * steps) | ~150 ms | ~7/s | American | ~1-2% |
bs, bs2, heston (todos >2.5k/s)binomial --steps 2000 (0.1% error)heston (~400 us, O(1))bates (~6 ms, O(1) con serie truncada)bs2 (3.6 us) — NO binomial (5.6 ms) ni lsm (150 ms)Regla para backtesting: las 3 closed-form (BS, BS2, Heston) corren ~300k ops/sec combinadas. Para 1M opciones se tarda ~3 seg. Suficiente para backtests de 1-2 semanas de data historica con multiples strikes/expiries.
industria. No funciona para americanas.
Cuando usar: cualquier opcion europea. Backtesting de masa (1M+ opciones/dia). Greeks analiticos (extension directa).
binomial)Arbol discreto. Soporta europeas Y americanas con ejercicio temprano. O(N^2), ~3 ms/op con N=500. Convergencia O(1/N).
Cuando usar: opciones americanas con precision ~0.5% (N=2000) o ~0.1% (N=10000). Tambien para validar la implementacion de BS convergiendo N->inf.
trinomial)Arbol con 3 branches (up/middle/down). Similar al binomial pero mejor condicionamiento numerico. ~1.5x mas lento que binomial para mismo N, pero necesita ~30% menos pasos para misma precision.
Cuando usar: cuando binomial da oscilaciones raras (T largo, sigma alto). Alternativa con convergencia mas estable.
mc)Simulacion. Solo europeas. O(paths), ~25 ms/op con paths=100k. Antithetic variates reduce varianza ~50-70% (factor 2-3x en samples efectivos).
Cuando usar: opciones path-dependent (asianas, barrier, lookback) — el skill no las implementa aun pero el framework es extensible. Validacion de BS con ruido MC. Opciones con payoffs custom.
lsm)Monte Carlo para americanas. Regresion least-squares sobre polinomios de S para estimar continuation value. O(paths * steps), ~50 ms/op con paths=100k, steps=50.
Cuando usar: opciones americanas con payoffs complejos donde BAW no aplica. Multi-asset american (basket options). Da lower bound del precio verdadero.
bs2)Closed-form aproximada para americanas. O(1), ~1.4 us/op — tan rapido
como BS. Error <1% vs binomial N=2000. Para q >= r cae a binomial
internamente.
Cuando usar: backtesting de opciones americanas en masa. Reemplazo de binomial cuando se necesita O(1) por opcion.
iv)Resuelve sigma_impl dado un precio de mercado observado. Bisection,
~0.6 ms por solve. Para europeas usa BS; para americanas usa binomial
N=500 como pricing engine.
Cuando usar: cuando tenes precios de mercado y queres inferir la volatilidad que el mercado esta priceando. Input para superficies de vol y modelos de vol estocastica.
pitm)Probabilidad bajo la medida risk-neutral Q (no real-world) de que
la opcion termine ITM al vencimiento. Closed-form via N(d2) / N(-d2),
~300 ns/op.
N(d2) para call, N(-d2) para putN(d2') con strike efectivo K +/- premiumCuando usar: filtrar trades con P(Profit) > X% en backtesting,
calcular expected value, comparar estrategias con misma prima pero
distinta P(Profit), sizing con Kelly criterion.
CUIDADO: la drift bajo Q es r - q, no la real. Para probabilidad
real-world, pasar la drift esperada como r (no la risk-free).
# Para cada dia historico:
# 1. Obtener IV actual (e.g. de yahoo-finance) y precio de mercado
# 2. Calcular precio teorico con IV_historica
# 3. Comparar contra precio de mercado
# Precio teorico con IV_historica
py scripts/option_pricing.py bs --S 580 --K 580 --T 0.08 --r 0.05 --sigma 0.18
# -> 12.34
# Precio teorico con IV_actual (subestimada por el mercado)
py scripts/option_pricing.py bs --S 580 --K 580 --T 0.08 --r 0.05 --sigma 0.22
# -> 15.67
# P&L esperado = (market - teorico) = 15.67 - 12.34 = +3.33 (long vol paga)# Para cada strike/expiry:
py scripts/option_pricing.py iv --S 580 --K 590 --T 0.08 --r 0.05 --price 8.50
# -> 0.2145 (la IV implicita de esa opcion)py scripts/option_pricing.py greeks --S 580 --K 580 --T 0.08 --r 0.05 --sigma 0.20 --json
# Devuelve {delta: 0.512, gamma: 0.018, vega: 0.85, theta: -0.12, rho: 0.31}
# Comprar 1000 opc + short 512 acciones = delta neutral# Opcion sobre indice con yield alto (e.g. SPX con div yield ~1.5%)
py scripts/option_pricing.py bs2 --S 5800 --K 5800 --T 0.25 --r 0.05 --q 0.015 \
--sigma 0.18 --type call --style american
# -> ~123.45 (vs BS europea ~120.10, premium de early exercise = $3.35)El modo all ejecuta todos los metodos compatibles con el --style elegido,
mas las utilidades (P(ITM), P(Profit), Greeks).
# Ejemplo 1: American put (Hull 21.1)
py scripts/option_pricing.py all --S 50 --K 50 --T 0.4167 --r 0.10 --sigma 0.40 \
--type put --style american
# Output (9 rows: BS, Binomial, Trinomial, MC, LSM, BS2, P(ITM), P(Profit), Greeks):
# +---------------------------+------------------+-----------------------+
# | Method | Config | Price / Value |
# +---------------------------+------------------+-----------------------+
# | Black-Scholes | closed-form O(1) | 4.076101 | (europea ref)
# | Binomial CRR | N=500 | 4.283160 |
# | Trinomial | N=500 | 4.283429 |
# | Monte Carlo | paths=100000 | 4.077892 +/- 0.0254 | (europea)
# | Longstaff-Schwartz | paths=100000 | 4.258337 |
# | BS2/BAW (closed-form) | O(1) | 4.283766 |
# | P(ITM) | N(d2) bajo Q | 0.4871 |
# | P(Profit) vs BS price | premio=4.0761 | 0.3588 |
# | Greeks (delta/gamma/vega) | BS closed-form | d=-0.3857 g=+0.0296 |
# +---------------------------+------------------+-----------------------+
# Ejemplo 2: European call (incluye Heston y Bates, NO incluye LSM/BS2)
py scripts/option_pricing.py all --S 100 --K 100 --T 0.25 --r 0.05 --sigma 0.20
# Output (9 rows: BS, Binomial, Trinomial, MC, Heston, Bates, P(ITM), P(Profit), Greeks):
# +---------------------------+----------------------------------------+-----------------------+
# | Method | Config | Price / Value |
# +---------------------------+----------------------------------------+-----------------------+
# | Black-Scholes | closed-form O(1) | 4.614997 |
# | Binomial CRR | N=500 | 4.613001 |
# | Trinomial | N=500 | 4.613999 |
# | Monte Carlo | paths=100000 | 4.620907 +/- 0.0295 |
# | Heston 1993 | v0=s2, k=2, th=s2, sv=0.3, rho=-0.5 | 4.577095 |
# | Bates 1996 | Heston + lam=1, mu_J=-0.05, sig_J=0.10 | 4.924756 |
# | P(ITM) | N(d2) bajo Q | 0.5299 |
# | P(Profit) vs BS price | premio=4.6150 | 0.3534 |
# | Greeks (delta/gamma/vega) | BS closed-form | d=+0.5695 g=+0.0393 |
# +---------------------------+----------------------------------------+-----------------------+Reglas del all:
--style american: incluye LSM y BS2 (los unicos metodos para americanas)--style european: incluye Heston y Bates (son european-only)Parametros default de Heston/Bates cuando se invoca via all (calibrados a
equity US tipico): v0=sigma^2, kappa=2, theta=v0, sigma_v=0.3, rho=-0.5, lambda=1, mu_J=-0.05, sigma_J=0.10. Para parametros custom, usar los
modos heston / bates directamente.
q.
Para dividendos discretos (e.g. fechas conocidas) usar q equivalente o
ajustar el modelo.numpy.random.Generator: crear uno solo y pasar la seed a
MC/LSM. El default de la CLI ya usa default_rng.np.vectorize(bs_price) o mapear
manualmente. La libreria numba puede dar 10-50x speedup adicional
si se compila JIT.T, r, q, sigma, K y solo variar S en el loop.El modo validate corre 10 casos de assets/validation_cases.json
(5 europeos, 4 americanos, 1 put-call parity) y reporta pass/fail:
py scripts/option_pricing.py validate
# === Black-Scholes European ===
# [OK] Hull 9th ed Example 15.6 (ATM call): got 4.7594, ref 4.7594
# [OK] ... (5/5 pass)
# === American (Binomial N=2000) ===
# [OK] Hull 9th ed Example 21.1: got 4.2841, ref 4.2841
# [OK] ... (4/4 pass)
# === Put-Call Parity (BS) ===
# [OK] C - P = 4.8770575499, S*exp(-qT) - K*exp(-rT) = 4.8770575499
# 0 failure(s)Para agregar casos custom, editar assets/validation_cases.json.
Las funciones se pueden importar directamente en Python:
from scripts.option_pricing import bs_price, binomial_price, mc_european_price, lsm_price, bs2_american_price, bs_greeks, implied_vol
# Pricing
c = bs_price(100, 100, 0.25, 0.05, 0.0, 0.20, "call") # 4.615
p = binomial_price(100, 100, 0.25, 0.05, 0.0, 0.20, 500, "put", "european")
# Greeks (solo BS)
g = bs_greeks(100, 100, 0.25, 0.05, 0.0, 0.20, "call")
# -> {"delta": 0.569, "gamma": 0.039, "vega": 19.64, "theta": -10.47, "rho": 13.08}
# Implied vol
iv = implied_vol(4.62, 100, 100, 0.25, 0.05, 0.0, "call", "european")
# -> 0.2003Todas las funciones son flat (sin clases), aceptan escalares y
devuelven floats. Para vectores, usar np.vectorize o comprehensions.
MIT
© gauss314, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (scripts, references, assets) in skills/option-pricing of gauss314/skills.
Open the folder on GitHubat commit 5156f81
Option Pricing next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Option Pricing this skillgauss314/skills | 247 | — | ~4.7k | Automated safety check: Pass | MIT | |
| Tushare Datazillionare/zillionare | 321 | 2 repos | ~2.3k | Automated safety check: Pass | None | |
| Quant Blog Writingzillionare/zillionare | 321 | — | ~895 | Automated safety check: Pass | None | |
| Elliott Wave Signal EngineHKUDS/Vibe-Trading | 35k | — | ~482 | Automated safety check: Pass | MIT | |
| Candlestick Pattern SignalsHKUDS/Vibe-Trading | 35k | — | ~468 | Automated safety check: Pass | MIT | |
| Event-Driven Sentiment SignalsHKUDS/Vibe-Trading | 35k | — | ~2.1k | Automated safety check: Pass | MIT |
zillionare/zillionare
面向中文自然语言的 Tushare 数据研究技能。用于把“看看这只股票最近怎么样”“帮我查财报趋势”“最近哪个板块最强”“北向资金在买什么”“给我导出一份行情数据”这类请求,转成可执行的数据获取、清洗、对比、筛选、导出与简要分析流程。适用于 A 股、指数、ETF/基金、财务、估值、资金流、公告新闻、板块概念与宏观数据等研究场景。
zillionare/zillionare
撰写文笔精炼、富有深度的量化交易博文,论点清晰、证据确凿、叙事层次更加丰富。适用于量化交易博文、因子研究、回测复盘、数据源排查、市场微观结构、策略原理、风险控制、职业观察、量化人物故事等选题。文章将聚焦具体角度,提供详实的大纲、证据规划及成稿,力求内容兼具思想深度与诚实性,而非单纯口号式宣传;同时,通过人物经历、引言、贡献及行业背景的融入,让文章更具可读性和吸引力。
HKUDS/Vibe-Trading
Detects Elliott Wave structures in price data with a Zigzag swing finder and Fibonacci checks, and turns completed waves into long, short or flat signals.
HKUDS/Vibe-Trading
Detects 15 classic candlestick patterns with vectorized pandas code and combines bullish and bearish scores into a long, short or flat trading signal.
HKUDS/Vibe-Trading
Scores news, announcements and macro events with the LLM, stores them in an event CSV and blends the decaying event signal with technical signals in signal_engine.py.
HKUDS/Vibe-Trading
Generates trading signals from the Ichimoku five-line system using Tenkan and Kijun crossovers, cloud position and cloud direction, implemented in pandas.
gauss314/skills
Academic backtesting framework for quantitative research. An agent skill from gauss314/skills.
gauss314/skills
Datos de Google Finance via batchexecute (API RPC interna sin auth ni API key).
gauss314/skills
History of Market (historyofmarket.com) — API publica con 88 datasets historicos de indices US desde 1871.
gauss314/skills
Datos macro y sociales de Argentina via la API oficial Series de Tiempo del Estado (apis.datos.gob.ar/series).
gauss314/skills
Morningstar Screener via API JSON publica: descarga masiva de 53 universes (102K+ listings, 39 paises, NYSE/Nasdaq/BCBA/etc) con 33 campos (precio, market cap, ratios, retornos…
gauss314/skills
Construcción y optimización cuantitativa de portafolios: Markowitz (scipy.optimize + Monte Carlo), Black-Litterman (prior CAPM, views absolutas/relativas, posterior bayesiano), HRP/HERC/NCO…
Categories
Pricing completo de opciones europeas y americanas. An agent skill from gauss314/skills. Option Pricing is an agent skill from gauss314/skills. Pricing completo de opciones europeas y americanas.
Option Pricing fits situations like: tasks that involve Trading and backtesting.
Run `npx skills add gauss314/skills --skill option-pricing -a claude-code`. Or copy the skill folder (skills/option-pricing in gauss314/skills) into .claude/skills/option-pricing in your project. Claude Code loads it when a task matches its description.
Run `npx skills add gauss314/skills --skill option-pricing -a codex`. Or copy the skill folder (skills/option-pricing in gauss314/skills) into .agents/skills/option-pricing in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gauss314/skills --skill option-pricing -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/option-pricing, .gemini/skills/option-pricing, .github/skills/option-pricing and .opencode/skills/option-pricing in your project.
Going by SKILL.md and its folder, Option Pricing needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Option Pricing is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.7k tokens (SKILL.md is roughly 19k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Option Pricing: Tushare Data (zillionare/zillionare, 321 stars), Quant Blog Writing (zillionare/zillionare, 321 stars), Elliott Wave Signal Engine (HKUDS/Vibe-Trading, 35k stars) and Candlestick Pattern Signals (HKUDS/Vibe-Trading, 35k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
gauss314 (a GitHub user) maintains it in gauss314/skills, which has 247 GitHub stars. The repository holds 32 skills in this directory. The repository was last updated on June 14, 2026.
Source: gauss314/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.