import requests

symbol = 'AAPL'

apikey = 'YOUR FMP API KEY'

url = 'https://financialmodelingprep.com/stable/quote'

params = {'apikey': apikey, 'symbol':symbol}

r = requests.get(url, params=params)

data = r.json()

data

[

{

"symbol": "AAPL",

"name": "Apple Inc.",

"price": 268.47,

"changePercentage": -0.48189,

"change": -1.3,

"volume": 45140079,

"dayLow": 266.82,

"dayHigh": 272.29,

"yearHigh": 277.32,

"yearLow": 169.21,

"marketCap": 3967020108000,

"priceAvg50": 252.2522,

"priceAvg200": 224.3975,

"exchange": "NASDAQ",

"open": 269.795,

"previousClose": 269.77,

"timestamp": 1762549202

}

]

Field

Description

symbol

Ticker symbol identifying the stock or security uniquely.

name

Full name of the company or financial instrument.

price

Current trading price of the stock in the market.

changePercentage

Percent change in stock price compared to previous close.

change

Absolute change in price since the last trading session.

volume

Total number of shares traded during the current session.

dayLow

Lowest price reached by the stock during today's trading day.

dayHigh

Highest price reached by the stock during today's trading day.

yearHigh

Highest price the stock has traded at in last year.

yearLow

Lowest price the stock has traded at in last year.

marketCap

Total market value of the company's outstanding shares.

priceAvg50

Average stock price over the past 50 trading days.

priceAvg200

Average stock price over the past 200 trading days.

exchange

Stock exchange where the security is listed and traded.

open

Opening price of the stock at the start of trading day.

previousClose

Final trading price at market close from previous day.

timestamp

Unix timestamp indicating the time of the quoted data.

d = data[0]

price = d.get('price')

priceAvg50 = d.get('priceAvg50')

priceAvg200 = d.get('priceAvg200')

if priceAvg50 > priceAvg200:

trend_signal = "Uptrend (bullish momentum)"

elif priceAvg50 < priceAvg200:

trend_signal = "Downtrend (bearish momentum)"

else:

trend_signal = "Neutral/transition"

if price > priceAvg50 and priceAvg50 > priceAvg200:

trend_signal = "Strong uptrend continuation"

elif price < priceAvg50 and priceAvg50 < priceAvg200:

trend_signal = "Strong downtrend continuation"

result = {

"symbol": data[0].get("symbol") if data and isinstance(data, list) and data else None,

"price": price if 'price' in locals() else None,

"priceAvg50": priceAvg50 if 'priceAvg50' in locals() else None,

"priceAvg200": priceAvg200 if 'priceAvg200' in locals() else None,

"trend": trend_signal

}

print(result)

year_high = d.get('yearHigh')

year_low = d.get('yearLow')

threshold_percentage = 0.03

resistance_threshold = year_high * (1 - threshold_percentage)

support_threshold = year_low * (1 + threshold_percentage)

if resistance_threshold <= price <= year_high:

sr_signal = f"Price is approaching resistance near yearly high {year_high:.2f}"

elif year_low <= price <= support_threshold:

sr_signal = f"Price is approaching support near yearly low {year_low:.2f}"

else:

sr_signal = "Price is not near significant resistance or support levels based on yearly range"

print (f'Price is {price:.2f}')

print (f'Yearly high is {year_high:.2f}')

print(sr_signal)

open_price = d.get('open')

previous_close = d.get('previousClose')

threshold_percentage = 0.03

if open_price is None or previous_close is None or previous_close == 0:

gap_signal = "Insufficient data to assess opening gap"

else:

price_diff = open_price - previous_close

price_diff_pct = price_diff / previous_close

if price_diff_pct >= threshold_percentage:

gap_signal = f"Gap up detected: Open price {open_price:.2f} is higher than previous close {previous_close:.2f}"

elif price_diff_pct <= -threshold_percentage:

gap_signal = f"Gap down detected: Open price {open_price:.2f} is lower than previous close {previous_close:.2f}"

else:

gap_signal = "No significant gap detected at open compared to previous close"

print(f'Open price is {open_price:.2f}')

print(f'Previous close is {previous_close:.2f}')

print(gap_signal)

day_high = d.get('dayHigh')

day_low = d.get('dayLow')

print(f'Day high is {day_high:.2f}')

print(f'Day low is {day_low:.2f}')

closeness_threshold_pct = 0.01

if day_high is None or day_low is None or price is None:

intraday_signal = "Insufficient data to assess intraday position"

else:

volatility_range = day_high - day_low

# Check if the day's range is very tight relative to current price

if price and price != 0 and volatility_range / price <= closeness_threshold_pct:

intraday_signal = (

f"Very tight intraday range: High-Low = {volatility_range:.2f} "

f"({(volatility_range / price) * 100:.2f}% of price)."

)

elif volatility_range == 0:

intraday_signal = "No intraday volatility detected as day high equals day low."

else:

position_in_range = (price - day_low) / volatility_range

if position_in_range > 0.8:

intraday_signal = f"Price {price:.2f} is near the intraday high {day_high:.2f}, indicating bullish strength."

elif position_in_range < 0.2:

intraday_signal = f"Price {price:.2f} is near the intraday low {day_low:.2f}, indicating bearish pressure."

else:

intraday_signal = f"Price {price:.2f} is within the intraday range {day_low:.2f} - {day_high:.2f}, neutral zone."

print(intraday_signal)

import pandas as pd

import numpy as np

import requests

import matplotlib.pyplot as plt

symbols = ['AAPL', 'MSFT', 'META', 'GOOG']

def fetch_quote(symbol):

url = 'https://financialmodelingprep.com/stable/quote'

params = {'apikey': apikey, 'symbol': symbol}

r = requests.get(url, params=params)

r.raise_for_status()

data = r.json()

return data[0] if isinstance(data, list) and data else {}

def compute_metrics(d):

price = d.get('price')

priceAvg50 = d.get('priceAvg50')

priceAvg200 = d.get('priceAvg200')

open_price = d.get('open')

previous_close = d.get('previousClose')

day_high = d.get('dayHigh')

day_low = d.get('dayLow')

year_high = d.get('yearHigh')

year_low = d.get('yearLow')

# Momentum using moving averages

if priceAvg50 is not None and priceAvg200 is not None:

momentum = priceAvg50 - priceAvg200

else:

momentum = np.nan

# Gap percentage from open vs previous close

if open_price is not None and previous_close not in (None, 0):

gap = (open_price - previous_close) / previous_close

else:

gap = np.nan

# Intraday volatility: normalized range

if day_high is not None and day_low is not None and price not in (None, 0):

volatility = (day_high - day_low) / price

else:

volatility = np.nan

# Proximity to resistance (year high): smaller distance -> closer

if year_high is not None and price is not None:

resistance = (year_high - price) / year_high

else:

resistance = np.nan

return {

'price': price,

'momentum': momentum,

'gap': gap,

'volatility': volatility,

'resistance': resistance

}

# Fetch and compute for all symbols

records = []

for sym in symbols:

d = fetch_quote(sym)

m = compute_metrics(d)

m['symbol'] = sym

records.append(m)

df_metrics = pd.DataFrame(records).set_index('symbol')

# Plot 4 subplots: momentum, gap, volatility, resistance

fig, axes = plt.subplots(2, 2, figsize=(12, 8), constrained_layout=True)

ax1, ax2, ax3, ax4 = axes.ravel()

# Momentum

df_metrics['momentum'].plot(kind='bar', ax=ax1, color='steelblue', title='Momentum (50MA - 200MA)')

ax1.axhline(0, color='gray', linewidth=0.8)

# Gap

(df_metrics['gap'] * 100).plot(kind='bar', ax=ax2, color='orange', title='Gap % (Open vs Prev Close)')

ax2.axhline(0, color='gray', linewidth=0.8)

ax2.set_ylabel('%')

# Volatility

(df_metrics['volatility'] * 100).plot(kind='bar', ax=ax3, color='seagreen',

title='Intraday Volatility % (High-Low / Price)')

ax3.set_ylabel('%')

# Resistance proximity

(df_metrics['resistance'] * 100).plot(kind='bar', ax=ax4, color='crimson',

title='Proximity to Resistance % (Year High - Price) / Year High')

ax4.set_ylabel('%')

plt.show()

screener_url = f'https://financialmodelingprep.com/stable/company-screener'

querystring = {"apikey":apikey, "marketCapMoreThan":100_000_000_000, "sector":"Technology", "country":"US", "exchange":"NASDAQ"}

resp = requests.get(screener_url, querystring).json()

df = pd.DataFrame(resp)

tickers = df['symbol'].to_list()

url = 'https://financialmodelingprep.com/stable/quote'

results = []

# Ensure list_of_tickers exists; fallback to symbols if necessary

# tickers = list_of_tickers if 'list_of_tickers' in locals() and list_of_tickers else []

for symbol in tickers:

try:

r = requests.get(url, params={"apikey": apikey, "symbol": symbol})

if r.status_code != 200:

continue

data = r.json()

if not isinstance(data, list) or not data:

continue

q = data[0]

price = q.get('price')

yh = q.get('yearHigh')

yl = q.get('yearLow')

if price is None or yh in (None, 0) or yl in (None, 0):

continue

# Proximity percentages (smaller is closer)

prox_high = (yh - price) / yh # 0 means at year high

prox_low = (price - yl) / yl # 0 means at year low

results.append({

"symbol": symbol,

"price": price,

"yearHigh": yh,

"yearLow": yl,

"prox_high": prox_high,

"prox_low": prox_low

})

except Exception:

continue

if not results:

print("No valid quotes retrieved.")

else:

dfq = pd.DataFrame(results)

# Closest to year high: smallest prox_high

top_high = dfq.sort_values('prox_high').head(3).copy()

# Closest to year low: smallest prox_low

top_low = dfq.sort_values('prox_low').head(3).copy()

print("Top 3 closest to Year High:")

for _, row in top_high.iterrows():

pct = max(row['prox_high'], 0) * 100

print(f"{row['symbol']}: {pct:.2f}% below year high")

print("\nTop 3 closest to Year Low:")

for _, row in top_low.iterrows():

pct = max(row['prox_low'], 0) * 100

print(f"{row['symbol']}: {pct:.2f}% above year low")